![[2602.20751] SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.20751] SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Summary
The paper presents SibylSense, a novel approach to adaptive rubric learning that enhances reward mechanisms in reinforcement learning through memory tuning and adversarial probing.
Why It Matters
SibylSense addresses critical challenges in reinforcement learning by improving the adaptability and effectiveness of rubrics used for evaluating open-ended generation tasks. This advancement could lead to more robust AI systems capable of generating higher quality outputs, which is essential in various AI applications.
Key Takeaways
- SibylSense adapts rubric generation using a tunable memory bank.
- The method improves the discriminative power of rubrics for RL tasks.
- Experiments show enhanced performance over static and non-adaptive baselines.
Computer Science > Computation and Language arXiv:2602.20751 (cs) [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 y...
