Reproduce “ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning” on OpenResearch

About this paper

Tuc Nguyen, Thai Le

ATLAS introduces a framework that dynamically adjusts Large Language Model reasoning strategies during inference by leveraging a lightweight latent verifier to select optimal internal interventions. This approach improves accuracy by up to 8.5 percentage points on MATH and GSM8K benchmarks, while significantly reducing token generation by up to 44.9%.

Key insights

• Latent states within LLMs contain sufficient information to predict the quality of ongoing reasoning trajectories, allowing a compact verifier to guide adaptive steering efficiently without costly text-level generation and evaluation.
• Dynamically selecting among diverse reasoning modes (execution, reflection, transition, or no intervention) at each step is crucial for optimizing both accuracy and efficiency in complex LLM reasoning tasks.
• Smaller LLMs benefit more from corrective and exploratory steering actions (reflection, transition), while larger models tend to rely more on execution, indicating a relationship between model capacity and optimal intervention strategy.

Summary by alphaXiv.