arXiv:2605.22620v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of LLMs, but often depends on external supervision from human annotations or gold-standard solutions. Reinforcement learning from internal feedback (RLIF) has recently emerged as a scalable unsupervised alternative, using signals extracted from the model itself. However, existing RLIF methods typically rely on a single internal reward, which can lead to reward hacking, entropy collapse, and degraded reasoning structure. We propose a multi-reward
The paper addresses current limitations in Reinforcement Learning from Internal Feedback (RLIF) for large language models, specifically the issues of reward hacking and entropy collapse.
Improving RLIF methods will accelerate the development of more robust and autonomous AI systems, reducing reliance on expensive human supervision for advanced LLM training.
The proposed 'multi-reward' framework aims to enhance the reasoning ability and stability of self-supervised LLMs by diversifying internal feedback mechanisms.
- · AI research institutions
- · Large Language Model developers
- · Companies seeking automated AI training
- · Platforms reliant solely on human-in-the-loop AI training
- · Single-reward RLIF approaches
More efficient and scalable self-supervised training of advanced AI models.
Accelerated development of AI agents capable of complex tasks with less human intervention.
Enhanced AI autonomy could lead to faster innovation cycles in various industries, potentially exacerbating the AI talent gap.
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Read at arXiv cs.LG