arXiv:2605.25381v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post-training of Large Language Models (LLMs). While policy optimization is driven by all sampled tokens under a globally broadcast scalar reward, the heterogeneous policy behaviors exhibited along trajectories are largely overlooked without differentiation. Existing works address this by credit allocation, including token-level advantage reweighting, and selective token optimization, however, the allocation criterion are principally stagnant throughout training
The increasing scale and complexity of LLMs necessitate more sophisticated training methods beyond basic scalar rewards to achieve performance gains.
Improved temporal scheduling for RLVR could significantly enhance the efficiency and effectiveness of post-training for LLMs, leading to more capable AI systems.
The focus shifts from solely credit allocation to a more nuanced temporal scheduling of rewards, potentially enabling LLMs to learn more efficiently from heterogeneous policy behaviors.
- · AI model developers
- · Cloud providers
- · Deep learning researchers
- · Companies relying on less efficient LLM training methods
More robust and efficient training of large language models for various applications.
Accelerated development and deployment of advanced AI agents and applications leveraging these improved LLMs.
Increased competition and innovation in the AI sector due to lower barriers to developing high-performing models.
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Read at arXiv cs.LG