Abstract:Reinforcement learning with verifiable rewards (RLVR) has emerged as a strong paradigm for enhancing the reasoning capabilities of massive language models (LLMs).
However, its effectiveness is considerably hindered by the prevalence of ineffective coaching knowledge: many sampled prompts yield response groups which can be both solely appropriate or completely incorrect, resulting in zero-variance rewards and restricted studying signals.
Recent state-of-the-art methods handle this challenge via intensive LLM rollouts to filter ineffective samples, however at the price of appreciable computational overhead. Alternative approaches, including predictive sampling and trajectory replay, aim to enhance data effectivity however typically remain inadequate and should introduce extra points equivalent to systematic bias or suboptimal constraints.
To handle these limitations, we propose Group Prioritized Off-Policy Optimization (POPO), a easy yet effective framework that absolutely exploits effective training batches without further rollout overhead. POPO comprises two key components: prioritized group replay and decoupled off-coverage optimization. The previous replaces ineffective on-policy groups with effective off-policy groups via a recency-primarily based replay mechanism that jointly considers sample high quality and the diploma of off-policiness.
To additional mitigate the off-coverage gap, POPO employs decoupled importance sampling to right off-coverage bias whereas maintaining stable coverage updates underneath consistent belief-area constraints. Empirical evaluations across diverse reasoning duties, including arithmetic, planning, and visible geometry, show that POPO substantially accelerates RL finetuning and achieves sturdy reasoning efficiency with significantly fewer rollouts.

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