Learning to Reason without External Rewards
Authors: name: Xuandong Zhao, name: Zhewei Kang, name: Aosong Feng, name: Sergey Levine, name: Dawn Song

Training large language models (LLMs) for complex reasoning via Reinforcement
Learning with Verifiable Rewards (RLVR) is effective but limited by reliance on
costly, domain-specific supervision. We explore Reinforcement Learning from
Internal Feedback (RLIF), a framework that enables LLMs to learn from intrinsic
signals without external rewards or labeled data. We propose Intuitor, an RLIF
method that uses a model's own confidence, termed self-certainty, as its sole
reward signal. Intuitor replaces external rewards in Group Relative Policy
Optimization (GRPO) with self-certainty scores, enabling fully unsupervised
learning. Experiments demonstrate that Intuitor matches GRPO's performance on
mathematical benchmarks while achieving superior generalization to
out-of-domain tasks like code generation, without requiring gold solutions or
test cases. Our findings show that intrinsic model signals can drive effective
learning across domains, offering a scalable alternative to RLVR for autonomous
AI systems where verifiable rewards are unavailable. Code is available at
https://github.com/sunblaze-ucb/Intuitor