Papers by Zhongxin Liu
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning (2026.acl-long)
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| Challenge: | Existing methods for code execution reasoning are limited by the difficulty of the training data. |
| Approach: | They propose a model that uses reinforcement learning to reward correct answers from execution traces. |
| Outcome: | The proposed model improves pass@1 by up to 5.9% on code generation tasks over strong baselines. |
SEK: Self-Explained Keywords Empower Large Language Models for Code Generation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have achieved impressive performance in code generation. |
| Approach: | They propose a technique that extracts and explicates the key terms in the problem description with the LLM itself. |
| Outcome: | The proposed technique improves the Pass@1 of DeepSeek-Coder-V2-Instruct from 85.4% to 93.3% on the humaneval benchmark. |
DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can replicate insecure patterns from training data. |
| Approach: | They propose a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module. |
| Outcome: | Experiments show that the framework improves the secure-and-correct generation rate by 11.9% over baselines. |
ReCode: Reinforcing Code Generation with Reasoning-Process Rewards (2026.acl-long)
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| Challenge: | Bringing process-level supervision into RL often neglects optimizing reasoning quality. |
| Approach: | They propose a framework for RL that integrates reasoning-process rewards with strict execution outcomes and a benchmark comprising preference pairs of superior and inferior reasoning processes. |
| Outcome: | The proposed framework outperforms the base version of ReCode by 16.1% and reaches performance comparable to GPT-4-Turbo. |
JumpCoder: Go Beyond Autoregressive Coder via Online Modification (2024.acl-long)
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| Challenge: | Existing code large language models lack reversibility and autoregressive sequential generation is incapable of correcting previous missing statements as humans do. |
| Approach: | They propose a model-agnostic framework that enables human-like online modification and non-sequential generation to augment code large language models. |
| Outcome: | The proposed framework enables human-like modification and non-sequential generation to augment code large language models. |