GPS trains a small model on optimization history to predict prompt difficulty and select intermediate-difficulty diverse batches, yielding better training efficiency, final performance, and test-time allocation than baselines on reasoning benchmarks.
Can prompt difficulty be online predicted for accelerating rl finetuning of reasoning models?arXiv preprint arXiv:2507.04632, 2025a
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Pilot-Commit estimates per-prompt informativeness via a pilot stage and skips low-variance prompts, matching baseline accuracy with up to 4.0x fewer cumulative rollouts than DAPO on math reasoning tasks.
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.
Listwise Policy Optimization explicitly performs target-projection on the LLM response simplex, unifying and improving group-based RLVR methods with monotonic improvement and flexible divergences.
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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Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
GPS trains a small model on optimization history to predict prompt difficulty and select intermediate-difficulty diverse batches, yielding better training efficiency, final performance, and test-time allocation than baselines on reasoning benchmarks.
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Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training
Pilot-Commit estimates per-prompt informativeness via a pilot stage and skips low-variance prompts, matching baseline accuracy with up to 4.0x fewer cumulative rollouts than DAPO on math reasoning tasks.
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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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Listwise Policy Optimization: Group-based RLVR as Target-Projection on the LLM Response Simplex
Listwise Policy Optimization explicitly performs target-projection on the LLM response simplex, unifying and improving group-based RLVR methods with monotonic improvement and flexible divergences.
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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.