2511.0023 ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning v1

🎯 ICAIS2025 Accepted Paper
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🎓 Meta Review & Human Decision

Decision:

Accept

Meta Review:

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📋 Summary

This paper introduces ReasoningV, a framework designed to enhance Verilog code generation using large language models (LLMs). The core contribution of this work is the creation of ReasoningV-5K, a high-quality dataset comprising 5,322 functionally verified Verilog samples, each accompanied by distilled reasoning paths. This dataset is intended to address the scarcity of high-quality, reasoning-focused training data for hardware description languages. The authors propose a two-stage training scheme to leverage this dataset. The first stage involves training Low-Rank Adaptation (LoRA) adapters on the OriGen dataset to acquire foundational Verilog knowledge. The second stage merges these adapters and fine-tunes all parameters on ReasoningV-5K to enhance the model's reasoning capabilities specific to hardware design. Furthermore, the paper introduces an adaptive reasoning mechanism, guided by a lightweight Judge Adapter, which dynamically allocates computational resources based on the perceived difficulty of the task. This mechanism aims to improve the efficiency of the model by using fewer tokens for simpler tasks and more tokens for complex ones. The empirical results presented in the paper demonstrate that the proposed approach achieves state-of-the-art performance among open-source models on several Verilog code generation benchmarks, including VerilogEval-human, VerilogEval-machine, and RTLLM. The authors also show significant token savings compared to commercial models, highlighting the efficiency of their adaptive reasoning mechanism. Overall, this paper presents a valuable contribution to the field of automated hardware design by providing a high-quality dataset and an effective training framework for Verilog code generation. The combination of a carefully curated dataset, a two-stage training approach, and an adaptive reasoning mechanism demonstrates a promising path towards more efficient and accurate LLM-based hardware design tools. The authors have clearly identified a gap in the existing literature and have made a significant effort to address it with a well-structured and empirically validated approach.

✅ Strengths

I find several aspects of this paper to be particularly strong. First and foremost, the creation of the ReasoningV-5K dataset is a significant contribution. The authors have clearly invested considerable effort in curating a dataset of 5,322 functionally verified Verilog samples, each paired with distilled reasoning paths. This addresses a critical need in the field, as high-quality, reasoning-focused datasets for hardware description languages are scarce. The rigorous verification process, which includes simulation-based testing, ensures the reliability of the dataset, making it a valuable resource for training and evaluating LLMs for Verilog code generation. The inclusion of reasoning paths is also a notable strength, as it provides explicit guidance for the model to learn the reasoning process behind the code, which is crucial for complex hardware design tasks. The two-stage training method is another well-motivated and empirically validated strength. The initial training of LoRA adapters on the OriGen dataset allows the model to acquire foundational Verilog knowledge, while the subsequent fine-tuning on ReasoningV-5K enhances its reasoning capabilities. This two-stage approach is effective in leveraging the strengths of both datasets and is supported by ablation studies that clearly demonstrate the complementary benefits of foundational knowledge and reasoning enhancement. The adaptive reasoning mechanism is also a novel and effective approach to improving efficiency. By dynamically allocating tokens based on task difficulty, the model achieves significant token savings while maintaining competitive performance. This is a crucial innovation, as it addresses the computational cost associated with LLMs, making them more practical for real-world hardware design applications. The experimental results presented in the paper are also compelling. The authors demonstrate that their approach achieves state-of-the-art performance among open-source models on several Verilog code generation benchmarks. The comprehensive evaluation, which includes comparisons with both open-source and commercial models, provides strong evidence for the effectiveness of the proposed framework. Finally, the paper is generally well-written and easy to follow. The methodology is clearly explained, and the experimental results are presented in a comprehensive manner, making it accessible to a broad audience. The authors have also made their code and dataset publicly available, which further enhances the impact and reproducibility of their work.

❌ Weaknesses

Despite the strengths of this paper, I have identified several weaknesses that warrant careful consideration. First, the technical novelty of the proposed approach is somewhat limited. While the creation of the ReasoningV-5K dataset is a significant contribution, the two-stage training scheme and adaptive reasoning mechanism are not particularly innovative from a methodological standpoint. The paper employs LoRA for the first stage and full parameter fine-tuning in the second stage, which are relatively common techniques in fine-tuning large language models. The core contribution seems to be in the application of these techniques to the Verilog domain, rather than a novel algorithmic advancement. The paper does not provide a deep theoretical analysis of why these methods are particularly effective for Verilog code generation, nor does it offer a detailed comparison to existing methods that use similar techniques in other domains. This lack of theoretical grounding and comparative analysis weakens the claim of significant algorithmic advancement. My confidence in this assessment is high, as the method description clearly outlines the use of LoRA and full parameter fine-tuning, which are standard techniques, and the paper does not provide a theoretical justification for their use in this specific context. Second, the paper lacks a thorough analysis of the dataset's characteristics. While the authors provide a high-level distribution of the dataset across hardware design categories and difficulty levels, they do not provide a detailed analysis of the types of Verilog constructs included, such as the proportion of sequential versus combinational logic, the prevalence of different types of finite state machines, or the complexity of the arithmetic units. This information is crucial for understanding the dataset's limitations and potential biases. Without this granular analysis, it is difficult to assess the generalizability of the results and to determine whether the dataset adequately covers the full spectrum of Verilog code. My confidence in this assessment is high, as the paper only provides category-level distribution and lacks detailed analysis of Verilog constructs and complexity within those categories. Third, the experimental evaluation could be more comprehensive. While the paper compares against some Verilog-specific models, it could benefit from a more extensive comparison with a broader range of state-of-the-art models, especially those employing different architectures or training techniques, such as reinforcement learning. The evaluation primarily relies on pass@k metrics, which, while important, do not provide a complete picture of the generated code's quality. The paper lacks metrics that assess functional correctness beyond simple pass/fail criteria, such as simulation-based verification metrics, as well as metrics that evaluate the synthesizability, coding standards adherence, and complexity of the generated code. Furthermore, the paper does not include an analysis of the model's performance on different types of Verilog code, such as those with varying levels of complexity or different design styles. My confidence in this assessment is high, as the evaluation section primarily uses pass@k metrics and compares against a limited set of Verilog-specific models. There's no explicit presentation of simulation-based verification metrics in the results tables. Fourth, the paper lacks a detailed analysis of the types of errors made by the model. Understanding the common failure modes would provide valuable insights for future improvements. Specifically, it is unclear whether the errors are primarily syntactic, semantic, or related to incorrect reasoning about hardware behavior. A breakdown of error types, such as incorrect module instantiations, flawed control flow, or improper handling of timing constraints, would be beneficial. The absence of error analysis limits the understanding of the model's weaknesses and hinders the identification of areas for improvement. My confidence in this assessment is high, as the paper lacks any section or discussion dedicated to error analysis. Fifth, the comparison with commercial models is limited. While the authors acknowledge the performance gap, a more detailed analysis of the strengths and weaknesses of ReasoningV compared to models like DeepSeek-R1 would be informative. This should include a discussion of the specific types of problems where ReasoningV excels or falls short compared to these models, and a quantitative comparison of resource usage, such as inference time and memory consumption. While a quantitative comparison exists, a deeper qualitative analysis of strengths and weaknesses on specific problem types and a broader comparison of resource usage would be beneficial. My confidence in this assessment is medium, as the paper provides quantitative comparison with DeepSeek-R1 on token usage but lacks a detailed qualitative analysis of strengths and weaknesses on specific problem types and a broader comparison of resource usage. Finally, the paper does not provide a detailed analysis of the computational resources required for training and inference. This information is crucial for assessing the practicality of the proposed approach. The authors should specify the hardware used for training, the training time, and the memory requirements for both training and inference. The lack of information on computational resources makes it difficult to assess the practicality and reproducibility of the approach. My confidence in this assessment is high, as the paper lacks any mention of hardware specifications, training time, or memory requirements.

💡 Suggestions

To address the identified weaknesses, I recommend several concrete improvements. First, the authors should conduct a more in-depth analysis of the technical novelty of their approach. While the creation of the dataset is a valuable contribution, they should provide a more detailed theoretical analysis of why the two-stage training scheme and adaptive reasoning mechanism are effective for Verilog code generation. This could involve comparing their approach to existing methods that use similar techniques in other domains and highlighting the specific modifications or insights that make their approach uniquely suited for Verilog. A theoretical analysis of why these methods are effective for Verilog code generation would also strengthen the paper. For example, the authors could explore the impact of the LoRA-based fine-tuning on the model's ability to learn Verilog-specific syntax and semantics, and how the adaptive reasoning mechanism improves the model's ability to handle complex design tasks. The authors should also provide a more detailed explanation of the Judge Adapter, including its architecture, training process, and how it is integrated with the LLM. Second, the authors should provide a more thorough analysis of the dataset's characteristics. This should include a detailed breakdown of the types of Verilog constructs present in the dataset, such as the proportion of sequential versus combinational logic, the prevalence of different types of finite state machines, and the complexity of the arithmetic units. This analysis should be presented in a clear and concise manner, using tables and figures to illustrate the key findings. Furthermore, the authors should discuss the potential biases in the dataset and how these biases might affect the model's performance. For example, if the dataset is heavily skewed towards certain types of Verilog constructs, the model might not generalize well to other types of Verilog code. The authors should also provide a detailed explanation of the data generation process, including the prompts used to generate the reasoning paths and the verification process used to ensure the correctness of the generated code. Third, the experimental evaluation should be significantly expanded to include a more comprehensive set of baselines and metrics. The authors should compare their approach to other state-of-the-art models specifically designed for Verilog code generation, including those using transformer-based architectures or reinforcement learning techniques. The evaluation should also include an analysis of the model's performance on different types of Verilog code, such as those with varying levels of complexity or different design styles. Furthermore, the evaluation should include metrics that assess the functional correctness of the generated code beyond simple pass/fail criteria, such as simulation-based verification metrics. The authors should also consider metrics that evaluate the quality of the generated code, such as its complexity, its adherence to coding standards, and its susceptibility to common hardware design flaws. For example, the authors could use static analysis tools to measure the complexity of the generated code or to identify potential timing violations. The authors should also consider using metrics that evaluate the efficiency of the generated hardware, such as the area and power consumption of the synthesized circuits. These additional metrics would provide a more complete picture of the model's performance and its suitability for real-world hardware design tasks. Fourth, the authors should conduct a more granular error analysis, categorizing errors based on their nature and root cause. This should include a detailed examination of the types of Verilog code that the model struggles with, such as complex state machines, arithmetic circuits, or memory controllers. For example, the authors could analyze whether the model tends to make more errors in generating code with specific control flow structures or with particular types of hardware components. Furthermore, the authors should investigate if the errors are due to a lack of understanding of the problem description, incorrect reasoning about hardware behavior, or simply a failure to generate syntactically correct Verilog code. This analysis should be accompanied by concrete examples of common errors and a discussion of potential mitigation strategies, such as incorporating more explicit reasoning steps into the training process or using techniques to improve the model's ability to understand complex natural language instructions. Fifth, the authors should provide a more detailed comparison with commercial models, including a quantitative analysis of the strengths and weaknesses of ReasoningV. This should include a discussion of the specific types of problems where ReasoningV excels or falls short compared to these models, and a quantitative comparison of resource usage, such as inference time and memory consumption. Finally, the authors should provide a detailed analysis of the computational resources required for training and inference, including the hardware used, the training time, and the memory requirements. This information is crucial for assessing the practicality of the proposed approach and for enabling other researchers to reproduce the results. The authors should also consider releasing the trained models and the dataset to the community to facilitate further research in this area.

❓ Questions

Based on my analysis, I have several questions that I believe would be beneficial for the authors to address. First, how does the performance of ReasoningV scale with larger models? Have you experimented with models larger than 14B parameters, and if so, what were the results? This is important to understand the potential for further improvement and the limitations of the current approach. Second, what are the primary limitations of the adaptive reasoning mechanism? Are there specific types of problems where it struggles to allocate the appropriate number of tokens, and if so, how can these limitations be addressed? This is crucial for understanding the robustness of the adaptive mechanism and its applicability to diverse hardware design tasks. Third, how does the quality of the reasoning paths in the ReasoningV-5K dataset affect the performance of the trained models? Have you experimented with different levels of reasoning path detail, and if so, what were the results? This is important to understand the impact of the reasoning paths on the model's learning process and to identify potential areas for improvement in the dataset. Fourth, could the authors provide more details on the creation and verification process of the ReasoningV-5K dataset? For example, how the samples were collected, what criteria were used to ensure quality, and how functional verification was performed? This is essential for assessing the reliability and generalizability of the dataset. Fifth, could the authors explain the rationale behind the two-stage training scheme and how it specifically enhances Verilog reasoning capabilities? Additionally, how does the adaptive reasoning mechanism work, and what are its advantages over fixed reasoning approaches? This is important for understanding the underlying mechanisms of the proposed approach and its effectiveness. Finally, could the authors provide more ablation studies to demonstrate the impact of each component in the proposed framework? For example, the effect of the two-stage training scheme, the adaptive reasoning mechanism, and the quality of the ReasoningV-5K dataset? This is crucial for understanding the contribution of each component and for identifying potential areas for further optimization.

📊 Scores

Soundness:2.75
Presentation:2.75
Contribution:2.5
Rating: 5.0

AI Review from ZGCA

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📋 Summary

The paper presents ReasoningV, a framework for Verilog code generation that combines: (1) ReasoningV-5K, a dataset of 5,322 functionally verified Verilog problem–reasoning–solution–testbench tuples with distilled, code-free reasoning paths (Sec. 3.1–3.1.3); (2) a two-stage training scheme where LoRA adapters first establish Verilog foundations and then full-parameter fine-tuning on ReasoningV-5K enhances intrinsic reasoning (Sec. 3.2); and (3) an adaptive reasoning mechanism using a lightweight Judge Adapter that classifies tasks as Easy/Medium/Hard and routes decoding with matched prompts and token budgets (Sec. 3.3). On VerilogEval-human, RV-14B achieves 73.9% pass@1 and RV-7B achieves 57.8% (Table 2 and Sec. 5.2), outperforming corresponding base models by large margins. The adaptive mode yields substantial token savings compared to both fixed-depth variants and a strong commercial reasoning model, while preserving most of the accuracy (Sec. 5.3.2, Table 5). Ablations show the complementary benefits of the two training stages (Table 4) and the efficiency benefits of adaptive routing (Table 5). The authors state that data, models, and code will be released.

✅ Strengths

  • High-quality dataset curation: From ~690K PyraNet samples to 5,322 functionally verified instances via multi-dimensional filtering, domain-specific redundancy elimination, expert-guided quality checks, and Icarus Verilog simulation (Sec. 3.1.1–3.1.3, Fig. 2). The dataset structure includes problem, distilled code-free reasoning path, solution, and testbench.
  • Leakage safeguards in reasoning distillation: schema-only prompts, regex guards against code tokens, exclusion of unit-test constants/golden outputs, and exact-match filtering to exclude evaluation prompts/metadata (Sec. 3.1.3).
  • Two-stage training is simple and effective: LoRA foundations then full-parameter reasoning enhancement, with clear ablation showing the stages are complementary (Table 4).
  • Adaptive reasoning tailored to HDL: A Judge Adapter routes tasks to Easy/Medium/Hard budgets (512/1280/4096 tokens) and achieves significant token savings (85–93% vs DeepSeek-R1; 32–75% vs forced modes) while maintaining accuracy (Sec. 5.3.2, Table 5).
  • Strong empirical gains across three benchmarks (VerilogEval-human/machine, RTLLM) with standard evaluation setup (pass@k unbiased estimator with n=10, Icarus verification, temp=0.2, top-p=0.95) (Sec. 5.1–5.2). RV-14B establishes a strong open-source SOTA on VEval-H among accessible models.
  • Clarity of motivation and pipeline: The paper explicitly targets three gaps (data quality, reasoning depth, efficiency), and the methodology addresses each in a modular way (Fig. 1). Qualitative examples of the reasoning flow (Fig. 4) and dataset distribution analysis (Fig. 5) aid understanding.

❌ Weaknesses

  • Reproducibility and transparency gaps: Crucial details are deferred to an appendix/repository (full hyperparameters, Judge training and labeling protocol, prompts, seeds) (Sec. 3.2, 3.3). The paper would benefit from including key configurations in the main text.
  • Limited statistical rigor: Results are reported without confidence intervals or multiple runs despite using pass@k with n=10, which may have high variance; no significance tests vs. baselines (Sec. 5.1–5.3).
  • Judge Adapter diagnostics are under-specified: No report of classification accuracy, calibration curves, confusion matrix, or ablated performance when misrouting occurs; no breakdown of accuracy by routed difficulty buckets (Sec. 3.3, 5.3.2).
  • Contamination/leakage audit is qualitative: While safeguards are described (Sec. 3.1.3), there is no quantitative audit for semantic overlap between training and evaluation (e.g., embedding-based near-duplicate removal), beyond exact-match filtering.
  • Interpretability of 'code-free' reasoning paths: The paper does not quantify how much these distilled reasoning traces contribute beyond generic planning scaffolds; an ablation where training is done without reasoning paths (or with code-ful traces) would isolate their effect.
  • Scope of verification: Simulation/testbench-based functional correctness is necessary but not sufficient for synthesizability, timing, or area; the paper notes this in limitations but experiments remain purely simulation-based (Sec. 6).

❓ Questions

  • Reasoning path ablation: Can you report results when training on the same dataset without reasoning paths, and with code-containing reasoning traces, to quantify the unique benefit of 'code-free' distilled paths?
  • Judge Adapter details: How are Easy/Medium/Hard labels obtained for training (heuristics/manual/weak supervision)? Please report its classification accuracy, calibration, confusion matrix, and the performance breakdown by predicted difficulty. What is the overhead of running the Judge (latency/tokens)?
  • Statistical reporting: Please add confidence intervals or standard errors for pass@k across benchmarks and ablations, and report results over multiple random seeds (n=10 samples per problem can be noisy).
  • Contamination audit: Beyond exact-match filtering, did you perform semantic de-duplication (e.g., embedding similarity) between ReasoningV-5K and benchmarks (VEval-H/M, RTLLM)? If so, what were the thresholds and removals?
  • Token budgeting sensitivity: How sensitive are results to the specific budgets (512/1280/4096) and prompts across difficulty levels? Please include a sensitivity analysis.
  • Per-category performance: Given the dataset category distribution (Sec. 4.2), can you provide per-category pass@1 and token usage to show where gains are concentrated?
  • Training details: Please provide in the main text (or ensure in the supplementary) the full hyperparameters, optimizer settings, LoRA ranks, training schedules, data splits, random seeds, and compute budget for both stages and the Judge.
  • Generalization beyond simulation: Have you tested synthesizability (e.g., with Yosys) or timing checks for a subset of generated designs? If not, can you outline feasibility and expected trade-offs?
  • CR=4.56: Please clarify the definition (e.g., compression ratio of reasoning traces) and how it is measured, and relate it to performance/efficiency.
  • Routing behavior: For misrouted 'Hard' tasks predicted as 'Easy', how much does pass@1 drop? Please add a robustness analysis to misclassification.

⚠️ Limitations

  • Dataset size and coverage: ReasoningV-5K, though carefully curated, may not cover the full diversity of HDL design tasks (acknowledged in Sec. 6).
  • Verification scope: Functional simulation ensures behavioral correctness for given testbenches but does not ensure synthesizability, timing, area, or power. Integrating synthesis/timing feedback would strengthen claims.
  • Potential dataset/model bias: The dataset is derived from PyraNet and LLM-generated testbenches/reasoning; biases in source content may propagate.
  • Risk of contamination: Despite safeguards (regex, no code in reasoning, exact-match exclusion), there remains a risk of semantic overlap; a quantitative audit would reduce this concern.
  • Adaptive routing failure modes: If the Judge Adapter underestimates difficulty, performance may degrade. The paper does not quantify robustness to misclassification.
  • Societal/ethical considerations: Generated hardware could inadvertently include security flaws or be used to create malicious circuits; licensing and provenance of source code should be handled carefully.

🖼️ Image Evaluation

Cross‑Modal Consistency: 32/50

Textual Logical Soundness: 23/30

Visual Aesthetics & Clarity: 8/20

Overall Score: 63/100

Detailed Evaluation (≤500 words):

1. Cross‑Modal Consistency

• Visual ground truth: Fig.1 overall framework (dataset→two‑stage→adaptive); Fig.2 data‑filtering pipeline with numbers; Fig.3 prompt→R1→verification→dataset; Fig.4 problem/thinking/code for a pipelined adder; Fig.5 five panes: Easy, Medium, Hard PCA scatters; a log‑scale category count line plot; a long colour legend.

• Major 1: Table numbering/caption mismatch. The item labelled “Table 2” describes filtering, but the HTML table shows model performance across benchmarks. Evidence: "Table 2: Filtering stages and criteria" vs columns "VEval‑H P@1 P@5 P@10".

• Major 2: In Sec. 5.2, “Table 3 presents a comprehensive comparison,” but the shown “Table 3” contains Easy/Medium/Hard/Adaptive token ablation, not the cross‑model comparison. Evidence: "Table 3 presents a comprehensive comparison" vs rows "Easy/Medium/Hard/Adaptive(7B)".

• Major 3: Fig. 5 caption/text claim density/trajectory overlays, but panels show plain scatter and a line chart with no visible trajectories/densities. Evidence: "overlays density and trajectory cues".

• Minor 1: Fig. 1 annotates “359% Efficiency Gain” while text emphasizes “85–93% token savings” and “32–75% vs fixed depth”; relation is unclear.

• Minor 2: Fig. 5 legend is detached; panels lack explicit sub‑labels (a/b/c…), making referencing ambiguous.

2. Text Logic

• No Major issues found.

• Minor 1: Undefined notation “CR=4.56” for ReasoningV‑5K in Intro; define (e.g., compression ratio?) and how computed. Evidence: "distilled reasoning paths (CR=4.56)".

• Minor 2: Dataset categories shift from “15 hardware domains” (filtering) to “18 hardware design categories” (analysis) without mapping explanation; add reconciliation. Evidence: "15 hardware domains" vs "18 hardware design categories".

• Minor 3: “text/image embeddings” in Sec. 4.3 is unclear given a text‑centric dataset; specify image sources or remove.

3. Figure Quality

• Major 1: Illegible at print size. Figs 1–4 contain dense text/code; many labels are unreadable at ≈100% zoom, especially Fig. 4’s code block and Fig. 2’s step annotations. Evidence: Fig. 4 shows full code with tiny font.

• Minor 1: Fig. 5’s legend text is very small; colour palette may be hard for colour‑blind readers; add markers or shapes in legend.

• Minor 2: Figure‑alone test: Fig. 2/3 require captions to understand arrows/filters; Fig. 5 needs per‑pane labels and clearer axis/cluster explanations.

Key strengths:

  • Clear problem framing (data quality, reasoning depth, efficiency).
  • Solid verification story (Icarus simulation) and ablations separating training stages and routing.
  • Competitive results and substantial token savings documented.

Key weaknesses:

  • Multiple table numbering/caption mismatches impede claim verification.
  • Several figures are not legible at print size; missing sub‑labels hurt navigation.
  • Minor notational and dataset‑taxonomy inconsistencies (CR, 15→18 categories) reduce clarity.

📊 Scores

Originality:3
Quality:3
Clarity:3
Significance:3
Soundness:3
Presentation:3
Contribution:3
Rating: 7

AI Review from SafeReviewer

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📋 Summary

The paper 'ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning' presents a framework designed to enhance the generation of Verilog code using Large Language Models (LLMs). The core contributions include the creation of a high-quality dataset, ReasoningV-5K, a two-stage training scheme, and an adaptive reasoning mechanism. The ReasoningV-5K dataset is constructed through a rigorous multi-stage filtering and verification process, starting from the PyraNet dataset, and includes simulation-verified Verilog code samples with distilled reasoning paths. The two-stage training scheme involves initial training with LoRA adapters to acquire foundational Verilog knowledge, followed by full-parameter fine-tuning on the ReasoningV-5K dataset to enhance reasoning capabilities. The adaptive reasoning mechanism uses a lightweight Judge Adapter to classify tasks by difficulty and dynamically allocate token budgets, aiming to improve efficiency. The authors report significant improvements in both pass@1 accuracy and token efficiency, with the adaptive reasoning mechanism achieving 85-93% token savings compared to commercial models. Despite these contributions, the paper faces several critical challenges, including potential data contamination, limited novelty in its methodological components, and a lack of detailed analysis of the dataset's characteristics and the adaptive reasoning mechanism's effectiveness. These issues raise questions about the robustness and generalizability of the proposed framework, and the true source of the observed performance gains.

✅ Strengths

The paper makes several valuable contributions to the field of Verilog code generation using LLMs. One of the key strengths is the creation of the ReasoningV-5K dataset, which is a high-quality, functionally verified collection of Verilog code samples. The dataset construction process is rigorous, involving multiple stages of filtering and verification to ensure the correctness and quality of the samples. This dataset is a significant resource for researchers and practitioners, as it addresses the common issue of low-quality training data in the domain of hardware description languages. The two-stage training scheme is another notable contribution. By first training LoRA adapters to acquire foundational Verilog knowledge and then performing full-parameter fine-tuning on the ReasoningV-5K dataset, the authors effectively enhance the model's reasoning capabilities. This approach is particularly useful for improving the model's performance on complex hardware tasks. The adaptive reasoning mechanism, which dynamically allocates token budgets based on task difficulty, is also a valuable innovation. This mechanism significantly reduces token consumption, achieving 85-93% savings compared to commercial models, while maintaining competitive performance. The paper's empirical results are impressive, demonstrating substantial improvements in pass@1 accuracy and token efficiency. The ablation studies provide insights into the effectiveness of each component of the framework, although these studies could be more comprehensive. Overall, the paper presents a well-structured and empirically validated approach to improving Verilog code generation, which is a challenging and important task in hardware design automation.

❌ Weaknesses

Despite the paper's strengths, several critical weaknesses and limitations are evident. One of the most significant concerns is the potential for data contamination in the ReasoningV-5K dataset. The dataset is constructed using the PyraNet dataset as a starting point, and the filtering process involves using DeepSeek-R1 for quality assessment and testbench generation. Given that DeepSeek-R1 is a more recent model, there is a risk that the generated reasoning paths and testbenches might inadvertently include information or patterns present in the evaluation benchmarks, particularly VerilogEval. This concern is further compounded by the fact that the paper does not provide a detailed comparison of the PyraNet and ReasoningV-5K datasets, making it difficult to assess the extent of the changes and the potential for contamination. The lack of a clear explanation of the filtering process and the specific criteria used for deduplication and quality assessment also hinders the reproducibility and transparency of the dataset construction. Another major weakness is the limited novelty of the methodological components. The two-stage training scheme, while effective, is a common practice in LLM training. Similarly, the use of a small, task-specific dataset (5.3k samples) for full fine-tuning of a 7B/14B parameter model raises questions about the generalizability of the learned reasoning capabilities. The paper does not provide a strong theoretical justification for why this approach is particularly effective for Verilog code generation, which is crucial for establishing the scientific basis of the method. The adaptive reasoning mechanism, while innovative in its application to Verilog, is not entirely novel. The Judge Adapter used for difficulty classification and dynamic token allocation is similar to existing techniques in LLM optimization. The paper does not provide a detailed comparison with these existing methods, which would have strengthened the claim of innovation. Additionally, the paper lacks a comprehensive evaluation of the adaptive reasoning mechanism. The ablation studies in Table 5 show that the adaptive mode sometimes underperforms fixed-depth reasoning modes, particularly in terms of pass@1 accuracy. This suggests that the difficulty classification might not be sufficiently accurate, and the token allocation might not always be optimal. The paper does not provide a detailed analysis of the accuracy of the Judge Adapter or the distribution of difficulty classifications, which are essential for understanding the effectiveness of the adaptive mechanism. The presentation of the paper also has several issues that affect its clarity and readability. The use of ambiguous abbreviations (e.g., CR, CT, RET) without clear definitions makes it difficult to follow the methodology and results. The filtering ratios in Table 2 do not sum up correctly, indicating potential errors in the dataset construction process. The term 'problem' is used inconsistently, sometimes referring to the natural language specification and sometimes to the code solution, which can lead to confusion. The paper also lacks a detailed analysis of the dataset's characteristics, such as the distribution of problem types, difficulty levels, and the complexity of the reasoning paths. This information is crucial for understanding the scope and limitations of the dataset. Finally, the paper does not provide a clear explanation of how the proposed framework addresses the initial challenges outlined in the introduction, particularly the issue of limited reasoning for complex hardware tasks. The connection between the dataset construction, the two-stage training, and the adaptive reasoning mechanism, and how they collectively enhance the model's reasoning capabilities, is not well-articulated. These weaknesses collectively raise concerns about the robustness and generalizability of the proposed framework, and the true source of the observed performance gains.

💡 Suggestions

To address the identified weaknesses and enhance the overall quality of the paper, several concrete and actionable improvements are recommended. First, the authors should conduct a thorough investigation of potential data contamination in the ReasoningV-5K dataset. This could involve a detailed comparison of the dataset with the evaluation benchmarks, particularly VerilogEval, to identify and mitigate any overlap. The authors should also provide a clear and detailed explanation of the filtering process, including the specific criteria used for deduplication and quality assessment. This would improve the transparency and reproducibility of the dataset construction. Second, the paper should include a more comprehensive analysis of the dataset's characteristics. This should cover the distribution of problem types, difficulty levels, and the complexity of the reasoning paths. The authors should also provide a detailed comparison of the PyraNet and ReasoningV-5K datasets, highlighting the key differences and the improvements achieved through the filtering process. Third, the authors should conduct more detailed ablation studies to isolate the impact of each component of the framework. Specifically, the paper should include a comparison of the full ReasoningV framework with models trained only with the ReasoningV-5K dataset and models using only the adaptive reasoning mechanism. This would help to clarify the contribution of each component to the overall performance gains. Fourth, the paper should provide a more in-depth analysis of the adaptive reasoning mechanism. This should include the accuracy of the Judge Adapter in classifying task difficulty, the distribution of difficulty classifications, and the correlation between token allocation and performance. The authors should also explore the impact of different token budgets on model performance and the potential for further optimization of the adaptive mechanism. Fifth, the presentation of the paper should be improved to enhance clarity and readability. The authors should define all abbreviations and technical terms, ensure consistency in the use of terminology, and correct any errors in the tables. The paper should also provide a more detailed explanation of the reasoning path distillation process, including examples of the prompts used and the resulting reasoning paths. Finally, the authors should discuss the limitations of the proposed approach and potential avenues for future research. This should include an analysis of the types of Verilog code that are still challenging for the model, the computational cost of the adaptive reasoning mechanism, and the potential for further improvements in efficiency and performance.

❓ Questions

1. Could the authors provide a detailed comparison of the PyraNet and ReasoningV-5K datasets, including the number of samples filtered out at each stage, the criteria used for filtering, and examples of samples that were discarded? This would help to assess the extent of the changes and the potential for data contamination.

2. How did the authors ensure that the reasoning paths and testbenches generated by DeepSeek-R1 do not contain information or patterns present in the evaluation benchmarks, particularly VerilogEval? What specific measures were taken to prevent label leakage?

3. Could the authors provide a more detailed explanation of the two-stage training scheme, including the specific hyperparameters used and the rationale behind the choice of LoRA for the first stage and full-parameter fine-tuning for the second stage? How does this approach differ from standard fine-tuning practices, and why is it particularly effective for Verilog code generation?

4. What is the accuracy of the Judge Adapter in classifying task difficulty? How does the distribution of difficulty classifications (Easy, Medium, Hard) look across the dataset? Could the authors provide examples of tasks that are classified as Easy, Medium, and Hard, and explain the criteria used for classification?

5. How does the adaptive reasoning mechanism handle tasks that are initially misclassified by the Judge Adapter? Is there a mechanism for re-evaluation or dynamic adjustment of token budgets during inference? Could the authors provide a more detailed analysis of the performance trade-offs between token savings and accuracy?

6. Could the authors provide a more detailed explanation of the reasoning path distillation process, including the specific prompts used and examples of the resulting reasoning paths? How does the distillation process ensure that the reasoning paths are concise and reproducible while retaining the essential information for Verilog code generation?

7. What are the specific limitations of the proposed approach, and what types of Verilog code are still challenging for the model? How does the computational cost of the adaptive reasoning mechanism compare to other methods, and what are the potential areas for further optimization?

📊 Scores

Soundness:2.25
Presentation:2.0
Contribution:2.0
Rating: 4.5

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