Reproduce “Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence” on OpenResearch

About this paper

Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, Di Niu

Guided by Gut (GG) is a computationally efficient Test-Time Scaling framework that enhances Large Language Model reasoning by leveraging intrinsic signals refined through reinforcement learning. This approach enables smaller models to achieve reasoning performance comparable to much larger models while significantly reducing computational resources, demonstrating up to 8x faster inference and 4-5x lower memory usage.

Key insights

• Large Language Models (LLMs) possess intrinsic signals, such as token-level confidence and novelty, that can effectively guide complex reasoning processes without requiring external verifier models.
• Reinforcement learning, specifically using a confidence-based reward function, can significantly calibrate and improve the reliability of an LLM's internal confidence estimations, making them more suitable for self-guided search.
• By leveraging these refined intrinsic signals, Test-Time Scaling can dramatically enhance reasoning performance of smaller models while achieving substantial reductions in computational resources, including memory and inference time.

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