The paper introduces Random-Reset Policy Optimization (RRPO) and Self-Reset Policy Optimization (SRPO) that use resets to enable more precise credit assignment in RL for language model reasoning, with SRPO outperforming GRPO and RRPO across benchmarks.
Convergence and sample complexity of first-order methods for agnostic reinforcement learning.arXiv preprint arXiv:2507.04406,
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Credit Assignment with Resets in Language Model Reasoning
The paper introduces Random-Reset Policy Optimization (RRPO) and Self-Reset Policy Optimization (SRPO) that use resets to enable more precise credit assignment in RL for language model reasoning, with SRPO outperforming GRPO and RRPO across benchmarks.