AtManRL: Towards Faithful Reasoning via Differentiable Attention Saliency
Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both contributes to and faithfully reflects the processes underlying the model's final answer, rather than merely accompanying it, remains challenging. We introduce AtManRL, a method that leverages differentiable attention manipulation to learn more faithful reasoning through reinforcement learning. By training an additive attention mask that identifies tokens in the CoT crucial for producing correct answers, we derive a saliency reward signal that encourages the model to generate reasoning traces that genuinely influence its final predictions. W...
| Authors | Max Henning Höth, Kristian Kersting, Björn Deiseroth et al. |
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| Primary category | cs.CL |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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