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LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self-evolve to improve how agents learn. However, realizing this prospect faces two obstacles. Joint search over coupled algorithmic components is diffi...
AlignOPSD is introduced, following the principle of aligning supervision before assigning credit, which outperforms both GRPO and StepOPSD across all eight backbone--aggregate-metric comparisons, improving on GRPO by 5.5--8.7 \% and ranking first in six.
Transition-wise Rubric Credit Assignment (TRCA) is proposed, which derives step-level supervision directly from action-induced transitions without learned evaluators or successful anchors for long-horizon tasks with sparse successful anchors.
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