Breaking the Self-Confirming Loop: Diagnosing and Mitigating Systemic Reward Bias in Self-Rewarding RL
arXiv:2510.08977v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) efficiently scales the reasoning ability of large language models (LLMs) but is bottlenecked by scarce labeled data. Reinforcement learning with intrinsic rewards (RLIR) offers a scalable alternative via self-rewarding, yet often suffers from instability and inferior performance. We trace this gap to a systemic bias in confidence-coupled self-rewarding: the model tends to over-reward high-confidence mistakes, forming a self-confirming loop. We quantify this feedback-loop bias with th
This research addresses a critical stability and performance bottleneck in self-rewarding reinforcement learning for large language models, a technique increasingly being adopted due to data scarcity.
Improving the stability and performance of self-rewarding RL can unlock greater scalability and reasoning capabilities for LLMs, accelerating their development and application across various sectors.
By identifying and mitigating 'systemic reward bias' and 'self-confirming loops', this work offers a path to more robust and reliable self-improving AI systems.
- · AI developers
- · Large Language Models (LLMs)
- · AI research institutions
- · SaaS providers leveraging LLMs
- · AI models reliant solely on scarce labeled data
- · Competitors with less stable self-rewarding systems
Higher quality and more scalable large language models become feasible due to improved self-rewarding mechanisms.
The improved reasoning capabilities of LLMs could accelerate research and development in other AI-driven fields.
More robust and autonomous AI agents could emerge, capable of self-correction and continuous learning in complex environments.
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