ACECODER: Acing Coder RL via Automated Test-Case Synthesis
Most progress in recent coder models has been driven by supervised
fine-tuning (SFT), while the potential of reinforcement learning (RL) remains
largely unexplored, primarily due to the lack of reliable reward data/model in
the code domain. In this paper, we address this challenge by leveraging
automated large-scale test-case synthesis to enhance code model training.
Specifically, we design a pipeline that generates extensive (question,
test-cases) pairs from existing code data. Using these test cases, we construct
preference pairs based on pass rates over sampled programs to train reward
models with Bradley-Terry loss. It shows an average of 10-point improvement for
Llama-3.1-8B-Ins and 5-point improvement for Qwen2.5-Coder-7B-Ins through
best-of-32 sampling, making the 7B model on par with 236B DeepSeek-V2.5.
Furthermore, we conduct reinforcement learning with both reward models and
test-case pass rewards, leading to consistent improvements across HumanEval,
MBPP, BigCodeBench, and LiveCodeBench (V4). Notably, we follow the R1-style
training to start from Qwen2.5-Coder-base directly and show that our RL
training can improve model on HumanEval-plus by over 25\% and MBPP-plus by 6\%
for merely 80 optimization steps. We believe our results highlight the huge
potential of reinforcement learning in coder models.
67a2d995c97974764a8c294c
Huaye Zeng
67a2d995c97974764a8c294d
Dongfu Jiang
claimed_verified
2025-02-05T10:14:14.136Z
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Dongfu Jiang
user
DongfuJiang
67a2d995c97974764a8c294e
Haozhe Wang
claimed_verified
2025-04-15T08:01:56.171Z
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Haozhe Wang
user
JasperHaozhe
67a2d995c97974764a8c294f
Ping Nie
admin_assigned
2025-02-05T10:18:51.897Z
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/avatars/9415510b598079973c2b0436ad12db9c.svg
Ping Nie
user
pingnieuk
67a2d995c97974764a8c2950
Xiaotong Chen
67a2d995c97974764a8c2951
Wenhu Chen
ACECODER: Acing Coder RL via Automated Test-Case Synthesis
Most progress in recent coder models has been driven by supervised
fine-tuning (SFT), while the potential of reinforcement learning (RL) remains
largely unexplored, primarily due to the lack of reliable reward data/model in
the code domain. In this paper, we address this challenge by leveraging
automated large-scale test-case synthesis to enhance code model training.
Specifically, we design a pipeline that generates extensive (question,
test-cases) pairs from existing code data. Using these test cases, we construct
preference pairs based on pass rates over sampled programs to train reward
models with Bradley-Terry loss. It shows an average of 10-point improvement for
Llama-3.1-8B-Ins and 5-point improvement for Qwen2.5-Coder-7B-Ins through
best-of-32 sampling, making the 7B model on par with 236B DeepSeek-V2.5.
Furthermore, we conduct reinforcement learning with both reward models and
test-case pass rewards, leading to consistent improvements across HumanEval,
MBPP, BigCodeBench, and LiveCodeBench (V4). Notably, we follow the R1-style
training to start from Qwen2.5-Coder-base directly and show that our RL
training can improve model on HumanEval-plus by over 25\% and MBPP-plus by 6\%
for merely 80 optimization steps. We believe our results highlight the huge
potential of reinforcement learning in coder models.
Automated test-case synthesis improves reward models and reinforces coding models, demonstrating significant performance gains.
supervised fine-tuning reinforcement learning automated large-scale test-case synthesis preference pairs Bradley-Terry loss reward models Llama-3.1-8B-Ins Qwen2.5-Coder-7B-Ins DeepSeek-V2.5 best-of-32 sampling HumanEval MBPP BigCodeBench LiveCodeBench R1-style training HumanEval-plus MBPP-plus