SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
SibylSense adapts rubric generation via memory tuning and adversarial probing, enhancing RL performance.
Key Findings
Methodology
SibylSense adapts a frozen rubric generator using a tunable memory bank. The memory is updated via verifier-based item rewards, alternating memory tuning with adversarial policy updates to produce rubric-satisfying candidate answers.
Key Results
- SibylSense generates more discriminative rubrics and improves downstream RL performance on two open-ended tasks. It achieved a 6% accuracy improvement on the RaR-Medicine dataset compared to static baselines.
- On the GovReport dataset, SibylSense outperformed few-shot rubrics, demonstrating stronger discriminative power.
- Adversarial candidate refresh further improved performance, especially on the RaR-Medicine dataset.
Significance
This study addresses the challenge of reward design in open-ended generation tasks through adaptive rubric generation. SibylSense is significant in academia and industry as it enhances RL model performance in multi-dimensional tasks, overcoming traditional rubric scaling issues.
Technical Contribution
SibylSense introduces a novel adaptive rubric generation method through memory tuning and adversarial probing. Unlike existing methods, it dynamically adjusts rubrics to adapt to policy changes.
Novelty
SibylSense is the first to combine memory tuning with adversarial probing for adaptive rubric generation. Compared to traditional methods, it dynamically adjusts rubrics to capture new quality dimensions.
Limitations
- The method may underperform in highly complex tasks as the memory bank updates might not capture all quality dimensions.
- Adversarial candidate refresh may increase computational costs.
Future Work
Future research could explore applying SibylSense to larger-scale tasks and optimizing the memory bank update mechanism for improved efficiency and accuracy.
AI Executive Summary
Designing aligned and robust rewards for open-ended generation tasks remains a key barrier in reinforcement learning post-training. Traditional rubrics are either costly or difficult to scale, leading to reward hacking issues. SibylSense offers an adaptive rubric generation method through memory tuning and adversarial probing. It adapts a frozen rubric generator using a tunable memory bank, alternating memory tuning with adversarial policy updates to produce rubric-satisfying candidate answers. This approach excels in two open-ended tasks, generating more discriminative rubrics and improving downstream RL performance. SibylSense holds significant value in both academia and industry, providing new directions for future research, particularly in handling complex tasks and optimizing memory bank updates.
Deep Analysis
Background
With the rise of large language models, the demand for open-ended generation tasks has increased. However, these tasks' success criteria are often task-dependent and multi-dimensional, making traditional rubrics insufficient.
Core Problem
The core problem is designing aligned and robust reward functions to guide RL models in open-ended generation tasks. Traditional methods are either costly or difficult to scale, leading to reward hacking issues.
Innovation
SibylSense's core innovation lies in generating adaptive rubrics through memory tuning and adversarial probing. Memory tuning updates the memory bank via verifier-based item rewards, while adversarial probing generates rubric-satisfying candidate answers to shrink discriminative gaps.
Methodology
- �� SibylSense adapts a frozen rubric generator using a tunable memory bank.
- �� The memory bank is updated via verifier-based item rewards.
- �� SibylSense alternates memory tuning with adversarial policy updates to produce rubric-satisfying candidate answers.
Experiments
Experiments were conducted on the RaR-Medicine and GovReport datasets, using Qwen3-32B as the rubric generator, Qwen3-8B as the answer model, and GPT-4o as the verifier. Results show SibylSense outperforms baselines on both datasets.
Results
SibylSense achieved a 6% accuracy improvement on the RaR-Medicine dataset and outperformed few-shot rubrics on the GovReport dataset. Adversarial candidate refresh further improved performance.
Applications
SibylSense can be applied to open-ended generation tasks requiring adaptive rubrics, such as document drafting, analytical reporting, and question answering systems.
Limitations & Outlook
The method may underperform in highly complex tasks as the memory bank updates might not capture all quality dimensions. Adversarial candidate refresh may increase computational costs.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking, and SibylSense is like a smart assistant helping you adjust recipes based on ingredients and taste. It remembers which spices work well together and suggests them the next time you cook. This way, it continuously learns and improves, helping you make tastier dishes.
ELI14 Explained like you're 14
Imagine you're playing a game, and SibylSense is like a super-smart helper guiding you to make better choices in the game. It remembers which strategies work and suggests them the next time you play. This way, you can perform better in the game and beat more opponents!
Glossary
Reinforcement Learning
A machine learning method that trains models through rewards and penalties to perform better in specific tasks.
Used in the paper to optimize models for open-ended generation tasks.
Rubric
A set of criteria used to evaluate the quality of generation tasks, usually consisting of multiple dimensions.
Used to guide RL model optimization.
Memory Tuning
A method to improve model performance by updating information in a memory bank.
Used for adaptive rubric generation.
Adversarial Probing
A method to test and improve models by generating challenging candidate answers.
Used to shrink discriminative gaps in rubric generation.
RaR-Medicine
A medical reasoning QA dataset used to evaluate open-ended generation tasks.
Used as one of the experimental datasets.
Open Questions Unanswered questions from this research
- 1 How to apply SibylSense to larger-scale tasks and optimize memory bank updates.
- 2 The impact of adversarial candidate refresh on computational costs and its optimization.
Applications
Immediate Applications
Document Drafting
SibylSense can be used to automatically generate high-quality documents, helping users improve work efficiency.
Long-term Vision
Smart Assistant
SibylSense can evolve into a smart assistant, helping users make better decisions in various tasks.
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.