AI Gets WEIRD LLMs learn reasoning solely by their own internal sense of confidence
AI Summary
The video discusses a new research paper from Berkeley titled “Learning to Reason Without External Rewards.” It explores a novel method of reinforcement learning for large language models (LLMs) that uses the model’s own confidence, or “self-certainty,” as the sole reward signal rather than relying on costly external verifiable rewards tied to task accuracy.
Key points covered include:
- Traditional reinforcement learning rewards models based on external task success (like correct answers or task completion).
- This paper proposes using the model’s confidence level in its answers as an intrinsic reward, which surprisingly yields effective learning.
- Confidence is measured by the divergence of output probability distribution from a uniform distribution, reflecting how consistent the model’s repeated answers are.
- The approach, called intuittor or RLIF, matches the performance of supervised methods on mathematical reasoning tasks and generalizes well to unseen tasks such as code generation and instruction following.
- This method reduces dependency on expensive curated datasets and external supervision.
- By relying on internal feedback, the model is shown to improve reasoning capabilities, promote structured reasoning, and resist reward exploitation tactics.
- The research supports the theory that pretrained LLMs possess latent capabilities that can be drawn out through reinforcement learning without new external rewards.
- The approach could enable scalable autonomous learning and skill acquisition in AI systems, pushing capabilities beyond limits imposed by human oversight.
Overall, the video highlights a promising step toward more autonomous and generalizable AI that can improve through introspection and intrinsic motivation rather than just external rewards.