ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning

The article introduces ATLAS (Adaptive Test-time Latent Steering), a framework that enhances reasoning efficiency in large language models (LLMs) by employing a trained verifier to dynamically adjust steering actions based on latent states during inference. ATLAS outperforms traditional decoding and fixed steering methods on various mathematical and coding benchmarks, achieving higher accuracy while significantly reducing token usage. This approach allows practitioners to implement adaptive reasoning controls without modifying model parameters or relying on additional inference-time processes, thereby improving the scalability and efficiency of LLM applications.