| Challenge: | Existing large language models struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. |
| Approach: | They propose a lightweight yet effective unit test generator that scales unit tests based on problem difficulty. |
| Outcome: | The proposed approach significantly improves performance on three benchmarks. |
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| Challenge: | Existing methods rely on unit tests to evaluate code correctness and provide rewards, but these methods are difficult to verify at scale. |
| Approach: | They propose a code reward model that leverages Monte Carlo Tree Search guided by LLMs to generate code snippets and judges execution traces to annotate code with reward signals. |
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The Program Testing Ability of Large Language Models for Code (2024.emnlp-industry)
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| Challenge: | Recent development of large language models (LLMs) for code shows promise in achieving code intelligence. |
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CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)
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Jianfeng Cai, Jinhua Zhu, Ruopei Sun, Kangwen Zhao, Dongyun Xue, Mingxiao Feng, Wengang Zhou, Houqiang Li
| Challenge: | Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases. |
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Z1: Efficient Test-time Scaling with Code (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, but this often entails longer contexts and numerous reasoning token costs. |
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Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs (2025.naacl-srw)
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| Challenge: | Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. |
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CodeContests+: High-Quality Test Case Generation for Competitive Programming (2025.findings-emnlp)
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| Challenge: | Competitive programming has become a key task for training and evaluating large language models . but test cases of competitive programming problems are often difficult to obtain . |
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A Comprehensive Survey on Learning from Rewards for Large Language Models: Reward Models and Learning Strategies (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly used to judge code, but their reliability remains poorly understood. |
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CodeJudge: Evaluating Code Generation with Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown promising performance in code generation, but how to reliably evaluate code generated by LLMs remains a challenging problem. |
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S*: Test Time Scaling for Code Generation (2025.findings-emnlp)
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Dacheng Li, Shiyi Cao, Chengkun Cao, Xiuyu Li, Shangyin Tan, Kurt Keutzer, Jiarong Xing, Joseph E. Gonzalez, Ion Stoica
| Challenge: | S* is the first hybrid test-time scaling framework that significantly improves the coverage and selection accuracy of generated code. |
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