@inproceedings{wu-etal-2026-amaris,
title = "{AMARIS}: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning",
author = "Wu, Peilin and
Zhang, Xinlu and
Wan, Kun and
Zhao, Wentian and
Wu, Gang and
Du, Xinya and
Chen, Zhiyu",
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.2152/",
pages = "45981--46052",
ISBN = "979-8-89176-462-0",
abstract = "Rubric-based reward shaping provides interpretable and editable reward signals for fine-tuning LLMs via reinforcement learning (RL), but existing adaptive rubric methods typically update criteria from local evidence such as the current batch or instance-level comparisons. This local view discards diagnostic information produced during training, making it difficult to track recurring failures, evaluate previous rubric edits, or raise standards once earlier criteria become saturated. We introduce AMARIS, a Memory-Augmented Rubric Improvement System that grounds rubric updates in longitudinal training evidence. AMARIS stores rollout analyses, step-level summaries, and rubric update records in a persistent evaluation memory, then retrieves recent and semantically relevant history to revise rubrics. We evaluate AMARIS across science, medicine, instruction following, and creative writing under both global and instance-specific rubric settings, with policy models from the Qwen and Llama families trained by GRPO and PPO. AMARIS improves over static, local-adaptive, and memory-ablated baselines, such as +2.8 points on GPQA-Diamond and +2.2 points on IFBench over the strongest baselines, while analysis shows that memory reduces oscillatory rubric edits and supports a progression from early failure correction to later curriculum advancement. The final rubric set that AMARIS discovers is itself reusable, and the online trajectory contributes measurably beyond it. AMARIS runs asynchronously alongside the normal RL loop, reducing blocking latency relative to synchronous rubric updates. The source code is available at https://github.com/qualidea1217/AMARIS."
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<abstract>Rubric-based reward shaping provides interpretable and editable reward signals for fine-tuning LLMs via reinforcement learning (RL), but existing adaptive rubric methods typically update criteria from local evidence such as the current batch or instance-level comparisons. This local view discards diagnostic information produced during training, making it difficult to track recurring failures, evaluate previous rubric edits, or raise standards once earlier criteria become saturated. We introduce AMARIS, a Memory-Augmented Rubric Improvement System that grounds rubric updates in longitudinal training evidence. AMARIS stores rollout analyses, step-level summaries, and rubric update records in a persistent evaluation memory, then retrieves recent and semantically relevant history to revise rubrics. We evaluate AMARIS across science, medicine, instruction following, and creative writing under both global and instance-specific rubric settings, with policy models from the Qwen and Llama families trained by GRPO and PPO. AMARIS improves over static, local-adaptive, and memory-ablated baselines, such as +2.8 points on GPQA-Diamond and +2.2 points on IFBench over the strongest baselines, while analysis shows that memory reduces oscillatory rubric edits and supports a progression from early failure correction to later curriculum advancement. The final rubric set that AMARIS discovers is itself reusable, and the online trajectory contributes measurably beyond it. AMARIS runs asynchronously alongside the normal RL loop, reducing blocking latency relative to synchronous rubric updates. The source code is available at https://github.com/qualidea1217/AMARIS.</abstract>
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%0 Conference Proceedings
%T AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
%A Wu, Peilin
%A Zhang, Xinlu
%A Wan, Kun
%A Zhao, Wentian
%A Wu, Gang
%A Du, Xinya
%A Chen, Zhiyu
%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 wu-etal-2026-amaris
%X Rubric-based reward shaping provides interpretable and editable reward signals for fine-tuning LLMs via reinforcement learning (RL), but existing adaptive rubric methods typically update criteria from local evidence such as the current batch or instance-level comparisons. This local view discards diagnostic information produced during training, making it difficult to track recurring failures, evaluate previous rubric edits, or raise standards once earlier criteria become saturated. We introduce AMARIS, a Memory-Augmented Rubric Improvement System that grounds rubric updates in longitudinal training evidence. AMARIS stores rollout analyses, step-level summaries, and rubric update records in a persistent evaluation memory, then retrieves recent and semantically relevant history to revise rubrics. We evaluate AMARIS across science, medicine, instruction following, and creative writing under both global and instance-specific rubric settings, with policy models from the Qwen and Llama families trained by GRPO and PPO. AMARIS improves over static, local-adaptive, and memory-ablated baselines, such as +2.8 points on GPQA-Diamond and +2.2 points on IFBench over the strongest baselines, while analysis shows that memory reduces oscillatory rubric edits and supports a progression from early failure correction to later curriculum advancement. The final rubric set that AMARIS discovers is itself reusable, and the online trajectory contributes measurably beyond it. AMARIS runs asynchronously alongside the normal RL loop, reducing blocking latency relative to synchronous rubric updates. The source code is available at https://github.com/qualidea1217/AMARIS.
%U https://aclanthology.org/2026.findings-emnlp.2152/
%P 45981-46052
Markdown (Informal)
[AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.2152/) (Wu et al., Findings 2026)
ACL