arXiv:2605.22156v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs). However, the sparsity of binary verifier rewards often leads to low efficiency and optimization instability. To stabilize training, existing methods typically impose token-level constraints relative to a reference policy. We identify that such constraints penalize deviations indiscriminately; this can flip verifier-determined direction when the policy attempts to outperform the reference, thereb
The paper addresses current challenges in scaling LLM reasoning capabilities with Reinforcement Learning amidst growing interest in autonomous AI.
Improving the efficiency and stability of LLM training for reasoning directly impacts the speed and feasibility of developing more capable AI agents.
The proposed 'One-Way Policy Optimization' method offers a more stable and efficient approach to training self-evolving LLMs, potentially accelerating advanced AI development.
- · AI research institutions
- · LLM developers
- · AI agent developers
- · AI infrastructure providers
More robust and efficient training of large language models for complex reasoning tasks.
Faster development and deployment of sophisticated AI agents capable of autonomous operation.
Accelerated erosion of white-collar workflows as increasingly capable AI agents become viable at scale.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG