SEAL, a training-free method, calibrates the reasoning process of Large Language Models by steering latent representations to reduce redundant thoughts, achieving up to 14.1% accuracy improvement and 50.4% token reduction across diverse benchmarks.
Large Language Model, Reasoning, Efficiency, Representation Learning, Latent Space Steering
Runjin Chen, Zhenyu Zhang, Junyuan Hong, Souvik Kundu, Zhangyang Wang
The University of Texas at Austin, Intel
Generated by grok-3
Background Problem
Large Language Models (LLMs) have shown remarkable success in complex reasoning tasks through extended chain-of-thought (CoT) reasoning, but recent studies highlight significant inefficiencies due to redundant reasoning traces, particularly excessive reflection and transition thoughts, which increase inference latency and degrade performance by diverting focus from essential reasoning paths. The key problem addressed is how to calibrate these flawed reasoning pathways to improve both accuracy and efficiency without requiring model retraining.
Method
The proposed method, SEAL (Steerable Reasoning Calibration), is a training-free framework designed to optimize the CoT reasoning process in LLMs by reducing redundant reflection and transition thoughts. Its core idea is to identify and manipulate reasoning patterns in the latent space using a steering vector that promotes execution thoughts over less productive ones. The implementation involves two stages: (1) an offline stage where a reasoning steering vector is extracted by categorizing thoughts into execution, reflection, and transition types using keyword-based rules on a small validation set (e.g., 1000 samples from Math500), and computing the vector as the difference between average representations of execution and non-execution thoughts (); (2) an on-the-fly inference stage where the steering vector is applied to adjust hidden states at specific layers during decoding (), with controlling intervention strength. Key points include its negligible computational overhead during inference and the transferability of the steering vector across tasks and models.
Experiment
The experiments were conducted on multiple LLMs (DeepSeek-R1-Distill-Qwen-1.5B, DeepSeek-R1-Distill-Qwen-7B, QwQ-32B-Preview) across diverse benchmarks (Math500, GSM8K, LiveCodeBench) to evaluate SEAL’s effectiveness and efficiency. The setup included comparing SEAL against baseline models and a token-level logits penalty method, using metrics like accuracy and token count with greedy and sampling-based decoding. Datasets were chosen for their varying difficulty and domain (math and coding), with Math500 providing a challenging testbed for hard problems. Results showed SEAL consistently improved accuracy by up to 14.1% on hard Math500 problems and reduced token usage by 11.8% to 50.4% across tasks, outperforming logits penalty methods which struggled with conceptual-level adjustments. The experimental design was comprehensive, including ablation studies on steering type, layer, and strength, revealing optimal mid-to-late layer interventions and a balanced steering strength (). However, while results matched expectations of efficiency and accuracy gains, the reliance on manually curated keyword rules for thought classification might limit scalability, and the transferability claims, though promising, were tested on a limited set of domains, raising questions about broader applicability.
Further Thoughts
The concept of steering reasoning in the latent space opens up intriguing possibilities for broader applications beyond CoT optimization, such as in multi-agent systems where coordinated reasoning could benefit from similar calibration to avoid redundant deliberation. I am particularly curious about integrating SEAL with reinforcement learning paradigms like RLHF (Reinforcement Learning from Human Feedback) to dynamically adjust steering vectors based on user feedback, potentially enhancing alignment and task-specific performance. However, a critical concern is the risk of over-optimization—suppressing reflection might be detrimental in tasks requiring deep introspection or ethical considerations, areas where LLMs already struggle. Comparing SEAL to other representation engineering approaches, such as sparse autoencoders mentioned in related works, could provide deeper insights into whether latent space interventions are universally superior to token-level methods, or if hybrid approaches might yield better results. Additionally, exploring the connection between SEAL’s steering mechanism and emergent abilities in LLMs could uncover whether such calibration influences unexpected capabilities at scale, potentially linking to scaling laws research.