Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs
arXiv:2606.00726v1 Announce Type: new Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation. Existing methods often rely on explicit behavior-level control, making them insufficiently adaptive when failures and required corrections vary across reasoning states, tasks, and models. To this end, we propose Latent Reward Steering (LRS), an adaptive inference-time framework that promotes cognitive behaviors by optimizing the sparse-autoencoder (SAE) latent states that implicitly carry them. Rather than relying o
The continuous drive to enhance the reasoning capabilities of large language models (LLMs) is leading to more sophisticated control mechanisms that go beyond explicit behavior-level adjustments.
This framework offers a novel approach to improving LLM reasoning by implicitly steering cognitive behaviors, potentially making AI systems more reliable and adaptable across diverse tasks and models.
The method of promoting desired cognitive behaviors in LLMs shifts from explicit, often rigid, controls to an adaptive, implicit optimization of latent states, allowing for more nuanced and context-aware reasoning.
- · AI developers
- · LLM-powered applications
- · Research institutions
- · Developers relying solely on explicit control methods
LLMs exhibit more intelligent and adaptive reasoning capabilities across a wider range of complex tasks.
The development of more robust and less failure-prone AI agents becomes feasible, accelerating their deployment in critical applications.
Increased public and industry trust in AI systems due to their enhanced reliability and contextual understanding, leading to broader integration.
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Read at arXiv cs.AI