VCD-RNK: Efficient Verilog Code Reranking

• VCD-RNK is a framework that recasts Verilog code generation as a semantic alignment problem to select the most functionally correct candidate.
• It utilizes dual-teacher distillation and LoRA tuning on Qwen3-4B to efficiently identify and rerank code candidates based on simulated test case reasoning.
• The method bypasses heavy test execution by integrating syntax checking and simulated correctness assessment, achieving pass@1 improvements of 10.4–25.8%.

VCD-RNK refers to a reranking approach for Verilog code generation based on semantic alignment and knowledge distillation, as introduced in "The Cream Rises to the Top: Efficient Reranking Method for Verilog Code Generation" (Yang et al., 24 Sep 2025). The framework is engineered to address the shortcomings of pass@k-based code generation methods by explicitly modeling the functional correctness alignment between natural language specifications and candidate Verilog implementations. VCD-RNK incorporates expert Verilog reasoning in three domains—code semantic analysis, test case generation, and correctness assessment—executed entirely in simulation during inference, thereby avoiding the heavy computational costs of test execution.

1. Semantic Alignment Formulation

The VCD-RNK system recasts Verilog code generation as a semantic alignment problem. Given a natural language specification $x$ and a set of candidate Verilog codes $\{\hat{y}_i\}$, the goal is to select the candidate that best realizes the required functionality. This is formalized by using a discriminator $F_\phi(x, \hat{y}_i)$ estimating

$$F_\phi(x, \hat{y}_i) = p(\text{correct} \mid x, \hat{y}_i; \phi)$$

where $\phi$ are the model parameters. The discriminator is trained via maximum likelihood using the loss

$$L_\text{disc}(\phi) = -\mathbb{E}_{(x, y)\sim D}\left[ c(y)\log F_\phi(x, y) + (1-c(y))\log(1-F_\phi(x, y)) \right]$$

with $c(y)$ indicating binary correctness (1 if correct, 0 otherwise). At inference, candidates are reranked and the final output is

$\hat{y}_* = \arg\max_{i} F_\phi(x, \hat{y}_i)$

This replaces pass@k enumeration and random candidate selection with a functional mapping that directly models specification-implementation consistency.

2. Architecture and Workflow of VCD-RNK

The core VCD-RNK architecture consists of a lightweight discriminator model (based on Qwen3-4B with LoRA parameter-efficient tuning). The training data (VerilogJudge-47K) is curated via a dual-teacher distillation process:

Step	Component	Purpose
Data Curation	Dual-teacher distillation	Produces labeled dataset with expert reasoning
Model Tuning	LoRA fine-tuning on Qwen3-4B	Efficient learning; updates small subset of parameters
Inference	Syntax checking, reranking	Filters out errored code, applies majority voting

Dual-teacher distillation utilizes two large teacher models: doubao-seed-1.6 (primary, $T_1$) and DeepSeek-R1-671B (fallback, $T_2$). Algorithm 1 (in the paper) describes a process in which candidates generated by a code generator $G$ are labeled via actual test execution, then reviewed by both teachers. Only those passing strict semantic tests are retained for downstream fine-tuning.

During inference, candidate codes are generated, syntax-checked, and then evaluated by $F_\phi$ in $m$ passes, yielding

$$F_\phi(x, \hat{y}_i) = \frac{1}{m} \sum_{j=1}^m \mathbb{1}[F^{(j)}_\phi(x, \hat{y}_i) = 1]$$

Candidates are then sorted and the top-scoring code is selected.

3. Expert Knowledge Distillation Process

VCD-RNK incorporates domain knowledge over three specific reasoning axes:

• Code Semantic Analysis: The model learns to interpret hardware semantics beyond syntactic features, mapping natural language constructs to functionally correct Verilog patterns. This is achieved by distilling teacher explanations about data flows, process synchronizations, and state management.
• Test Case Generation (in simulation): Instead of executing comprehensive testbenches at runtime, the model learns to simulate the test case reasoning. During training, candidate codes were evaluated by test execution, but during inference, correctness is inferred by the model’s internal simulation of likely edge cases and input/output behaviors.
• Functional Correctness Assessment: The discriminator merges code analysis with simulated test reasoning to estimate functional alignment, giving robustness against both underspecified requirements and superficial code deviations.

This multi-faceted reasoning is built into the discriminator not as explicit rule-based operations, but by learning from teacher-provided explanations and example labels. The result is a system capable of rapidly and reliably ranking code candidates for functional accuracy without incurring real-time test execution cost.

4. Efficient Candidate Reranking and Inference

The inference process is specifically designed for computational efficiency:

1. Candidate generation (typically $k$ codes per prompt).
2. Syntax checking (filtering candidates for parse errors).
3. Simulated reasoning across $m$ voting passes (with $m$ usually modest; latency is $~$1.5 seconds per example).
4. Final reranking, selecting highest probability candidate.

This design bypasses the latency and compute burden of existing approaches reliant on pass@k test execution, making VCD-RNK suitable for production-scale integration and batch-mode synthesis. Empirical results in the paper demonstrate a pass@1 improvement of 10.4–25.8% over baseline LLMs, supporting both higher reliability and lower operational cost.

5. Broader Applications and Implications

VCD-RNK’s core methodology—semantic alignment modeling, dual-teacher knowledge distillation, and simulated reasoning—offers extensibility beyond Verilog code generation. The principles and workflow may be applied to other hardware description languages and specialized domains where functional specification is tightly coupled to code semantics (e.g., VHDL, SystemC, or even domain-specific synthesis languages).

Key practical impacts include:

• Reliability: VCD-RNK provides high-confidence outputs suitable for engineering contexts where only one correct solution is desired, not a shortlist.
• Efficiency: Eliminates testbench execution during candidate selection, supporting rapid iteration and batch design flows.
• Integrability: Designed to interface with LLMs and standard hardware design pipelines for practical deployment.
• Domain Transferability: The underlying reasoning and alignment mechanisms can serve as a template for bespoke reranking models in other domains.

6. Future Directions and Considerations

Several extensions and open questions are suggested by the design and performance of VCD-RNK:

• Expansion to other hardware languages and more complex specifications may be feasible if corresponding dual-teacher datasets can be collected.
• Refinement of simulated reasoning for other dynamic properties (timing, concurrency, synthesis constraints) could further improve correctness estimation.
• Integration with holistic design verification suites may enable end-to-end automated design pipelines for both functional synthesis and formal verification.
• Adaptation to other domains where code generation requires expert judgment and functional alignment could extend the impact of VCD-RNK’s methodology.

In summary, VCD-RNK provides a domain-optimized framework for Verilog code reranking via semantic alignment, efficiently incorporating expert knowledge and simulated reasoning to achieve reliable, high-performance code generation for hardware design applications (Yang et al., 24 Sep 2025).