PCM uses success-failure action variance to probabilistically select and mask chunks for gradient updates in GRPO, matching standard success rates with 2.38x wall-clock speedup and 60% lower memory on LIBERO benchmarks.
NGRPO: Negative-enhanced group relative policy optimization
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EDAS modulates advantage signals in RLVR to penalize repeated errors more and rare errors less, yielding consistent gains on math benchmarks when added to existing methods.
MCPO fixes vanishing training signals and shrinking weights in GRPO by using a hinge-KL regularizer on mastered prompts and prioritizing majority-correct prompts, yielding higher pass@1 and pass@k on math tasks.
citing papers explorer
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Learn Where Outcomes Diverge: Efficient VLA RL via Probabilistic Chunk Masking
PCM uses success-failure action variance to probabilistically select and mask chunks for gradient updates in GRPO, matching standard success rates with 2.38x wall-clock speedup and 60% lower memory on LIBERO benchmarks.
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Leveraging Error Diversity in Group Rollouts for Reinforcement Learning
EDAS modulates advantage signals in RLVR to penalize repeated errors more and rare errors less, yielding consistent gains on math benchmarks when added to existing methods.
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MCPO: Mastery-Consolidated Policy Optimization for Large Reasoning Models
MCPO fixes vanishing training signals and shrinking weights in GRPO by using a hinge-KL regularizer on mastered prompts and prioritizing majority-correct prompts, yielding higher pass@1 and pass@k on math tasks.
- Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation