ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning
arXiv:2606.08088v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models (LLMs), yet it remains limited by sparse binary rewards and its ignorance of model-internal uncertainty. In this paper, we propose ConSteer-RL, a simple yet effective framework that integrates token-level confidence signals derived from model log-probabilities into RLVR training. Specifically, building upon the Group Relative Policy Optimization (GRPO) framework, we construct a confidence-aware re
The rapid advancement and deployment of LLMs necessitate more robust and nuanced training methods to overcome current limitations in reasoning and reliability, especially given increasing compute availability.
Improving LLM reasoning through confidence-aware reinforcement learning enhances the practical utility and robustness of generative AI, accelerating its integration into critical applications.
The ability to integrate token-level confidence into LLM training provides a more granular and effective method for steering model behavior, moving beyond binary rewards to improve reliability and reduce hallucination.
- · AI developers
- · LLM-powered application providers
- · Researchers in reinforcement learning
- · Companies relying on less sophisticated LLM integration
- · Models prone to high-confidence factual errors
LLMs exhibit improved reasoning capabilities and reduced factual errors due to more sophisticated training.
Enhanced LLM reliability accelerates the adoption of AI agents in complex professional tasks, fostering greater trust in automated systems.
The widespread deployment of highly reliable AI agents begins to significantly reshape white-collar workflows and the economics of professional services, increasing demand for compute and specialized data.
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