@inproceedings{huang-etal-2026-internalizing,
title = "Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models",
author = "Huang, Shiting and
Li, Zecheng and
Zeng, Yu and
Ren, Qingnan and
Fang, Zhen and
Su, Qisheng and
Shi, Kou and
Zhang, Ziao and
Chen, Lin and
Chen, Zehui and
Zhao, Feng",
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.963/",
pages = "20370--20389",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose **M**eta-**E**xperience **L**earning (**MEL**), a novel framework that incorporates self-distilled meta-experience into the model{'}s parametric memory. Building upon standard RLVR, we introduce an additional design that leverages the LLM{'}s self-verification capability to conduct contrastive analysis on paired correct and incorrect trajectories, identify the precise bifurcation points where reasoning errors arise, and summarize them into generalizable meta-experience. The meta-experience is further internalized into the LLM{'}s parametric memory by minimizing the negative log-likelihood, which induces a language-modeled reward signal that bridges correct and incorrect reasoning trajectories and facilitates effective knowledge reuse. Experimental results demonstrate that MEL achieves consistent improvements on benchmarks, yielding 3.92{\%}{--}4.73{\%} Pass@1 gains across varying model sizes."
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<abstract>Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose **M**eta-**E**xperience **L**earning (**MEL**), a novel framework that incorporates self-distilled meta-experience into the model’s parametric memory. Building upon standard RLVR, we introduce an additional design that leverages the LLM’s self-verification capability to conduct contrastive analysis on paired correct and incorrect trajectories, identify the precise bifurcation points where reasoning errors arise, and summarize them into generalizable meta-experience. The meta-experience is further internalized into the LLM’s parametric memory by minimizing the negative log-likelihood, which induces a language-modeled reward signal that bridges correct and incorrect reasoning trajectories and facilitates effective knowledge reuse. Experimental results demonstrate that MEL achieves consistent improvements on benchmarks, yielding 3.92%–4.73% Pass@1 gains across varying model sizes.</abstract>
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%0 Conference Proceedings
%T Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models
%A Huang, Shiting
%A Li, Zecheng
%A Zeng, Yu
%A Ren, Qingnan
%A Fang, Zhen
%A Su, Qisheng
%A Shi, Kou
%A Zhang, Ziao
%A Chen, Lin
%A Chen, Zehui
%A Zhao, Feng
%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 huang-etal-2026-internalizing
%X Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as an effective approach for enhancing the reasoning capabilities of Large Language Models (LLMs). Despite its efficacy, RLVR faces a meta-learning bottleneck: it lacks mechanisms for error attribution and experience internalization intrinsic to the human learning cycle beyond practice and verification, thereby limiting fine-grained credit assignment and reusable knowledge formation. We term such reusable knowledge representations derived from past errors as meta-experience. Based on this insight, we propose **M**eta-**E**xperience **L**earning (**MEL**), a novel framework that incorporates self-distilled meta-experience into the model’s parametric memory. Building upon standard RLVR, we introduce an additional design that leverages the LLM’s self-verification capability to conduct contrastive analysis on paired correct and incorrect trajectories, identify the precise bifurcation points where reasoning errors arise, and summarize them into generalizable meta-experience. The meta-experience is further internalized into the LLM’s parametric memory by minimizing the negative log-likelihood, which induces a language-modeled reward signal that bridges correct and incorrect reasoning trajectories and facilitates effective knowledge reuse. Experimental results demonstrate that MEL achieves consistent improvements on benchmarks, yielding 3.92%–4.73% Pass@1 gains across varying model sizes.
%U https://aclanthology.org/2026.findings-emnlp.963/
%P 20370-20389
Markdown (Informal)
[Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models](https://aclanthology.org/2026.findings-emnlp.963/) (Huang et al., Findings 2026)
ACL
- Shiting Huang, Zecheng Li, Yu Zeng, Qingnan Ren, Zhen Fang, Qisheng Su, Kou Shi, Ziao Zhang, Lin Chen, Zehui Chen, and Feng Zhao. 2026. Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20370–20389, Budapest, Hungary. Association for Computational Linguistics.