arXiv:2606.08815v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models. However, existing methods based on Group Relative Policy Optimization (GRPO) rely on a binary outcome reward, which induces two structural failure modes: Zero-Advantage Collapse, in which all rollouts in a group share the same outcome and the gradient vanishes, and Hallucinated Certainty, in which the model becomes increasingly confident on incorrect rollouts late in training. We address both mode
This research addresses fundamental limitations in current reinforcement learning methods for improving reasoning in large language models, indicating a maturing field and a focus on critical bottlenecks.
Improved reasoning capabilities in large language models are crucial for their broader adoption in complex problem-solving, impacting a wide range of AI applications and industrial automation.
The proposed 'Momentum for Reasoning' method offers a more stable and effective approach to training language models for long-chain reasoning, potentially accelerating the development of more capable AI agents.
- · AI researchers
- · Large language model developers
- · Companies adopting advanced AI
- · AI agents
- · Legacy RL policy optimization methods
- · Applications requiring robust reasoning from current LLMs
More robust and less error-prone large language models will emerge for tasks requiring complex reasoning.
This could accelerate the deployment of AI agents in sensitive domains where verifiable reasoning is paramount.
The enhanced reasoning capabilities might lead to breakthroughs in scientific discovery and automated problem-solving across various industries.
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Read at arXiv cs.LG