@inproceedings{zheng-etal-2026-gradients,
title = "Gradients Know What Outcomes Don{'}t: Unlocking Reinforcement Learning for {LLM} Reasoning with Gradient-Aligned Rewards",
author = "Zheng, Leqi and
Su, Jinbo and
Niu, Fang and
Wang, Chaokun and
Wang, Weiping and
Zhang, Jiajun and
Yan, Shannan and
Wu, Jie and
Kang, Zhaolu and
Fu, Rong and
Zhang, Hang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.594/",
pages = "13686--13706",
ISBN = "979-8-89176-459-0",
abstract = "Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct trajectories. Existing dense reward alternatives, from surface heuristics to process reward models, either ignore the expert solutions already present in training corpora or require expensive offline annotation. We propose \textbf{Gradient-Aligned Reward (GAR)}, which operates in the policy{'}s own gradient space: truncated backpropagation through the output projection layer extracts a compact gradient vector for each rollout, and cosine similarity with an expert-anchor gradient yields a dense, reasoning-aware reward with less than 9{\%} wall-clock overhead. We prove that this cosine admits a multiplicative decomposition into prediction-error and activation-pattern factors, providing a concrete characterization of what the alignment signal measures. On Qwen3-4B and Qwen3-8B, GAR consistently improves over GRPO and other baselines on competition-level math benchmarks and transfers to GPQA Diamond and MMLU-Pro without domain-specific data. Code and data are available at \url{ https://github.com/LQgdwind/GAR }."
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<abstract>Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct trajectories. Existing dense reward alternatives, from surface heuristics to process reward models, either ignore the expert solutions already present in training corpora or require expensive offline annotation. We propose Gradient-Aligned Reward (GAR), which operates in the policy’s own gradient space: truncated backpropagation through the output projection layer extracts a compact gradient vector for each rollout, and cosine similarity with an expert-anchor gradient yields a dense, reasoning-aware reward with less than 9% wall-clock overhead. We prove that this cosine admits a multiplicative decomposition into prediction-error and activation-pattern factors, providing a concrete characterization of what the alignment signal measures. On Qwen3-4B and Qwen3-8B, GAR consistently improves over GRPO and other baselines on competition-level math benchmarks and transfers to GPQA Diamond and MMLU-Pro without domain-specific data. Code and data are available at https://github.com/LQgdwind/GAR .</abstract>
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%0 Conference Proceedings
%T Gradients Know What Outcomes Don’t: Unlocking Reinforcement Learning for LLM Reasoning with Gradient-Aligned Rewards
%A Zheng, Leqi
%A Su, Jinbo
%A Niu, Fang
%A Wang, Chaokun
%A Wang, Weiping
%A Zhang, Jiajun
%A Yan, Shannan
%A Wu, Jie
%A Kang, Zhaolu
%A Fu, Rong
%A Zhang, Hang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zheng-etal-2026-gradients
%X Reinforcement learning from verifiable rewards (RLVR) drives chain-of-thought reasoning in large language models, yet its binary outcome reward cannot distinguish among correct trajectories. Existing dense reward alternatives, from surface heuristics to process reward models, either ignore the expert solutions already present in training corpora or require expensive offline annotation. We propose Gradient-Aligned Reward (GAR), which operates in the policy’s own gradient space: truncated backpropagation through the output projection layer extracts a compact gradient vector for each rollout, and cosine similarity with an expert-anchor gradient yields a dense, reasoning-aware reward with less than 9% wall-clock overhead. We prove that this cosine admits a multiplicative decomposition into prediction-error and activation-pattern factors, providing a concrete characterization of what the alignment signal measures. On Qwen3-4B and Qwen3-8B, GAR consistently improves over GRPO and other baselines on competition-level math benchmarks and transfers to GPQA Diamond and MMLU-Pro without domain-specific data. Code and data are available at https://github.com/LQgdwind/GAR .
%U https://aclanthology.org/2026.emnlp-main.594/
%P 13686-13706
Markdown (Informal)
[Gradients Know What Outcomes Don’t: Unlocking Reinforcement Learning for LLM Reasoning with Gradient-Aligned Rewards](https://aclanthology.org/2026.emnlp-main.594/) (Zheng et al., EMNLP 2026)
ACL
- Leqi Zheng, Jinbo Su, Fang Niu, Chaokun Wang, Weiping Wang, Jiajun Zhang, Shannan Yan, Jie Wu, Zhaolu Kang, Rong Fu, and Hang Zhang. 2026. Gradients Know What Outcomes Don’t: Unlocking Reinforcement Learning for LLM Reasoning with Gradient-Aligned Rewards. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13686–13706, Budapest, Hungary. Association for Computational Linguistics.