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arXiv:2610.35480v1 Announce Type: cross Abstract: Multi-reward reinforcement learning offers a promising framework for aligning large language models (LLMs) with diverse objectives, but conflicting r...
arXiv:2606. 16771v1 Announce Type: new Abstract: As LLMs advance, post-training reinforcement learning (RL) increasingly relies on multi-dimensional rewards to cultivate comprehensive capabilities.
The paper introduces Personalized Group Relative Policy Optimization (P‑GRPO), a new alignment framework for large language models that separates advantage estimation from immediate batch statistics. By normalizing advantages using preference‑group‑specific reward histories instead of the concurrent generation group, P‑GRPO maintains contrastive signals for distinct user preferences. Experiments across various tasks show that P‑GRPO converges faster and yields higher rewards than standard GRPO, improving alignment with heterogeneous human preferences while preserving general capabilities.
arXiv:2606. 06080v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards is widely used for post-training reasoning models, but sparse outcome rewards make exploration difficult.
arXiv:2605. 13217v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge.
The paper introduces CONCUR, an advantage estimator designed for multi‑task, multi‑reward reinforcement learning with large language models. CONCUR replaces standardized scores of gated auxiliary rewards with adjusted scores that ensure higher primary rewards never receive lower scores and that responses at the primary reward ceiling receive equal scores. Two variants, CONCUR‑T and CONCUR‑A, are evaluated on models across math, code, instruction following, and writing tasks, outperforming all baselines and the approach of dropping all gated auxiliaries.