@inproceedings{bai-etal-2026-towards,
title = "Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization",
author = "Bai, Fei and
Chen, Zhipeng and
Hao, Chuan and
Yang, Ming and
Tao, Ran and
Dai, Bryan and
Zhao, Xin and
Yang, Jian and
Xu, Hongteng",
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.774/",
pages = "16363--16383",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement learning from verifiable rewards (RLVR) has become a promising paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing RL-based training remains only a rough approximation to human learning, where humans leverage external experience to guide exploration and gradually internalize problem-solving strategies into stable knowledge. Motivated by this gap, we ask: how can LLMs better utilize and internalize experience during RLVR training? To answer this question, we propose \textbf{D}ual \textbf{G}uidance \textbf{O}ptimization{~}(\textbf{DGO}), which treats experience as a bridge between external guidance and internal policy learning. Rather than using experience only as an inference-time aid or trajectories only as optimization signals, DGO couples them in a closed loop: experience guides exploration, improved trajectories renew the experience bank, and guided trajectories are rewritten as self-contained traces for policy distillation. This enables the model to better utilize external experience while yielding consistent gains under experience-free inference. Experiments across multiple benchmarks show that DGO consistently outperforms baselines under both experience-free and experience-guided inference, suggesting that coupling utilization and internalization leads to effective reasoning."
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<abstract>Reinforcement learning from verifiable rewards (RLVR) has become a promising paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing RL-based training remains only a rough approximation to human learning, where humans leverage external experience to guide exploration and gradually internalize problem-solving strategies into stable knowledge. Motivated by this gap, we ask: how can LLMs better utilize and internalize experience during RLVR training? To answer this question, we propose Dual Guidance Optimization (DGO), which treats experience as a bridge between external guidance and internal policy learning. Rather than using experience only as an inference-time aid or trajectories only as optimization signals, DGO couples them in a closed loop: experience guides exploration, improved trajectories renew the experience bank, and guided trajectories are rewritten as self-contained traces for policy distillation. This enables the model to better utilize external experience while yielding consistent gains under experience-free inference. Experiments across multiple benchmarks show that DGO consistently outperforms baselines under both experience-free and experience-guided inference, suggesting that coupling utilization and internalization leads to effective reasoning.</abstract>
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%0 Conference Proceedings
%T Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization
%A Bai, Fei
%A Chen, Zhipeng
%A Hao, Chuan
%A Yang, Ming
%A Tao, Ran
%A Dai, Bryan
%A Zhao, Xin
%A Yang, Jian
%A Xu, Hongteng
%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 bai-etal-2026-towards
%X Reinforcement learning from verifiable rewards (RLVR) has become a promising paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing RL-based training remains only a rough approximation to human learning, where humans leverage external experience to guide exploration and gradually internalize problem-solving strategies into stable knowledge. Motivated by this gap, we ask: how can LLMs better utilize and internalize experience during RLVR training? To answer this question, we propose Dual Guidance Optimization (DGO), which treats experience as a bridge between external guidance and internal policy learning. Rather than using experience only as an inference-time aid or trajectories only as optimization signals, DGO couples them in a closed loop: experience guides exploration, improved trajectories renew the experience bank, and guided trajectories are rewritten as self-contained traces for policy distillation. This enables the model to better utilize external experience while yielding consistent gains under experience-free inference. Experiments across multiple benchmarks show that DGO consistently outperforms baselines under both experience-free and experience-guided inference, suggesting that coupling utilization and internalization leads to effective reasoning.
%U https://aclanthology.org/2026.findings-emnlp.774/
%P 16363-16383
Markdown (Informal)
[Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization](https://aclanthology.org/2026.findings-emnlp.774/) (Bai et al., Findings 2026)
ACL
- Fei Bai, Zhipeng Chen, Chuan Hao, Ming Yang, Ran Tao, Bryan Dai, Xin Zhao, Jian Yang, and Hongteng Xu. 2026. Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 16363–16383, Budapest, Hungary. Association for Computational Linguistics.