DARE co-evolves difficulty estimation and policy in RL for LLMs to improve training efficiency, final performance, and inference speed by using tailored strategies for different difficulty levels.
BOTS: A unified framework for Bayesian online task selection in LLM reinforcement finetuning
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A teacher-driven sampling method selects appropriately difficult questions for student models in GRPO-based RL to improve reasoning performance under fixed compute on OpenMathReasoning.
citing papers explorer
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DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
DARE co-evolves difficulty estimation and policy in RL for LLMs to improve training efficiency, final performance, and inference speed by using tailored strategies for different difficulty levels.
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Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
A teacher-driven sampling method selects appropriately difficult questions for student models in GRPO-based RL to improve reasoning performance under fixed compute on OpenMathReasoning.