SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
#SibylSense #Adaptive Rubric Learning #Memory Tuning #Adversarial Probing #Reinforcement Learning #Open-ended Generation #Reward Design #AI Supervision
📌 Key Takeaways
- Researchers developed SibylSense, an adaptive rubric learning approach using memory tuning and adversarial probing
- The system addresses challenges in creating aligned rewards for reinforcement learning post-training
- SibylSense uses a tunable memory bank of validated rubric items that can be updated dynamically
- Experiments showed improved performance over static and non-adaptive baselines
📖 Full Retelling
Researchers led by Yifei Xu and including Guilherme Potje, Shivam Shandilya, and ten other collaborators introduced SibylSense, an innovative adaptive rubric learning approach via memory tuning and adversarial probing, in a paper submitted to arXiv on February 24, 2026, addressing the persistent challenge of creating aligned and robust rewards for open-ended generation in reinforcement learning post-training. SibylSense represents a significant advancement in addressing the difficulties of rubric construction for AI systems. The approach adapts a frozen rubric generator through a tunable memory bank of validated rubric items, which is updated using verifier-based item rewards measured by reference-candidate answer discriminative gaps from limited examples. The system alternates between memory tuning and a rubric-adversarial policy update that produces rubric-satisfying candidate answers, effectively shrinking discriminative gaps and driving the rubric generator to capture new quality dimensions that might have been missed in static approaches. The research team demonstrated through experiments on two open-ended tasks that SibylSense produces more discriminative rubrics compared to static and non-adaptive methods, leading to improved downstream reinforcement learning performance. This addresses critical issues in current AI systems where expert rubrics are costly to develop, prompted rubrics often lack depth or consistency, and fixed-pool discriminative rubrics can become saturated and drift over time, potentially enabling reward hacking.
🏷️ Themes
Artificial Intelligence, Machine Learning, Reinforcement Learning
📚 Related People & Topics
Reinforcement learning
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
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Connections for Reinforcement learning:
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Large language model
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Artificial intelligence
6 shared
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Machine learning
4 shared
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Science Publishing Group
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Reasoning model
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Original Source
--> Computer Science > Computation and Language arXiv:2602.20751 [Submitted on 24 Feb 2026] Title: SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing Authors: Yifei Xu , Guilherme Potje , Shivam Shandilya , Tiancheng Yuan , Leonardo de Oliveira Nunes , Rakshanda Agarwal , Saeid Asgari , Adam Atkinson , Emre Kıcıman , Songwu Lu , Ranveer Chandra , Tusher Chakraborty View a PDF of the paper titled SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing, by Yifei Xu and 11 other authors View PDF Abstract: Designing aligned and robust rewards for open-ended generation remains a key barrier to RL post-training. Rubrics provide structured, interpretable supervision, but scaling rubric construction is difficult: expert rubrics are costly, prompted rubrics are often superficial or inconsistent, and fixed-pool discriminative rubrics can saturate and drift, enabling reward hacking. We present SibylSense, an inference-time learning approach that adapts a frozen rubric generator through a tunable memory bank of validated rubric items. Memory is updated via verifier-based item rewards measured by reference-candidate answer discriminative gaps from a handful of examples. SibylSense alternates memory tuning with a rubric-adversarial policy update that produces rubric-satisfying candidate answers, shrinking discriminative gaps and driving the rubric generator to capture new quality dimensions. Experiments on two open-ended tasks show that SibylSense yields more discriminative rubrics and improves downstream RL performance over static and non-adaptive baselines. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.20751 [cs.CL] (or arXiv:2602.20751v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.20751 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yifei Xu [ view email ] [v1] Tue, 24 Feb 2026 10:28...
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