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Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
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fine-tuningreinforcement learningcode generation
2609.11956
Builder Relevance
1h ago80%
Abstract
This paper explores the feasibility of offline post-training for code-generating LLMs using existing datasets to enhance performance without the need for new sample generation.
Reality Card
Core Claim
Offline reinforcement learning can significantly improve zero-shot code generation performance of LLMs with minimal training time and without online sampling.
Method / Result
Performance gains observed across models ranging from 0.5B to 7B parameters.
Limitations
The extent of improvement varies among model families, which may affect generalizability.
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