@inproceedings{li-etal-2026-latent-reward,
title = "Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning {LLM}s",
author = "Li, Jiakang and
Zhu, Guanyu and
Jin, Can and
Huang, Chenxi and
Yu, Dexu and
Chen, Ronghao and
Zhou, Yang and
Peng, Hongwu and
Lan, Xuanqi and
Metaxas, Dimitris N. and
Li, Youhua",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.655/",
pages = "13748--13767",
ISBN = "979-8-89176-462-0",
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 on predefined cognitive behaviors or steering directions derived from them, LRS trains a latent reward model on reasoning traces by final answer correctness to estimate the quality of intermediate latent states. During inference, reward gradients provide state-specific correction directions for fragile latent states, while a reward and confidence gate restricts intervention to states the reward signal flags as fragile. Experiments on multiple reasoning LLM backbones and benchmarks show that LRS consistently improves performance over various baselines, and post-hoc analyses further indicate that LRS implicitly promotes good cognitive behaviors that fix the original reasoning errors. Codes are available at: https://anonymous.4open.science/r/Latent-Reward-Steering-5B56."
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<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 on predefined cognitive behaviors or steering directions derived from them, LRS trains a latent reward model on reasoning traces by final answer correctness to estimate the quality of intermediate latent states. During inference, reward gradients provide state-specific correction directions for fragile latent states, while a reward and confidence gate restricts intervention to states the reward signal flags as fragile. Experiments on multiple reasoning LLM backbones and benchmarks show that LRS consistently improves performance over various baselines, and post-hoc analyses further indicate that LRS implicitly promotes good cognitive behaviors that fix the original reasoning errors. Codes are available at: https://anonymous.4open.science/r/Latent-Reward-Steering-5B56.</abstract>
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%0 Conference Proceedings
%T Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs
%A Li, Jiakang
%A Zhu, Guanyu
%A Jin, Can
%A Huang, Chenxi
%A Yu, Dexu
%A Chen, Ronghao
%A Zhou, Yang
%A Peng, Hongwu
%A Lan, Xuanqi
%A Metaxas, Dimitris N.
%A Li, Youhua
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-latent-reward
%X 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 on predefined cognitive behaviors or steering directions derived from them, LRS trains a latent reward model on reasoning traces by final answer correctness to estimate the quality of intermediate latent states. During inference, reward gradients provide state-specific correction directions for fragile latent states, while a reward and confidence gate restricts intervention to states the reward signal flags as fragile. Experiments on multiple reasoning LLM backbones and benchmarks show that LRS consistently improves performance over various baselines, and post-hoc analyses further indicate that LRS implicitly promotes good cognitive behaviors that fix the original reasoning errors. Codes are available at: https://anonymous.4open.science/r/Latent-Reward-Steering-5B56.
%U https://aclanthology.org/2026.findings-emnlp.655/
%P 13748-13767
Markdown (Informal)
[Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs](https://aclanthology.org/2026.findings-emnlp.655/) (Li et al., Findings 2026)
ACL
- Jiakang Li, Guanyu Zhu, Can Jin, Chenxi Huang, Dexu Yu, Ronghao Chen, Yang Zhou, Hongwu Peng, Xuanqi Lan, Dimitris N. Metaxas, and Youhua Li. 2026. Latent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13748–13767, Budapest, Hungary. Association for Computational Linguistics.