REC RL improves LLM code generation by automatically assessing and optimizing requirement difficulty with adaptive curriculum sampling, yielding 1.23-5.62% Pass@1 gains over baselines.
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Proposes a two-stage on-the-fly input adaptation framework to reduce mispredictions in code language models across understanding tasks without retraining or additional supervision.
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Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning
REC RL improves LLM code generation by automatically assessing and optimizing requirement difficulty with adaptive curriculum sampling, yielding 1.23-5.62% Pass@1 gains over baselines.
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On-the-Fly Input Adaptation for Reliable Code Intelligence
Proposes a two-stage on-the-fly input adaptation framework to reduce mispredictions in code language models across understanding tasks without retraining or additional supervision.