4 papers · 1 filter
QUBRIC: Co-Designing Queries and Rubrics for RL Beyond Verifiable Rewards
Rongzhi Zhang, Rui Feng, Zhihan Zhang +8
Rubric-based RL is a promising route for extending reinforcement learning beyond verifiable rewards, yet existing methods optimize rubrics while treating the query distribution as…
RNR: Teaching Large Language Models to Follow Roles and Rules
Kuan Wang, Alexander Bukharin, Haoming Jiang +9
Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models t…
Data Diversity Matters for Robust Instruction Tuning
Alexander Bukharin, Shiyang Li, Zhengyang Wang +6
Recent works have shown that by curating high quality and diverse instruction tuning datasets, we can significantly improve instruction-following capabilities. However, creating su…
Situated Natural Language Explanations
Zining Zhu, Haoming Jiang, Jingfeng Yang +6
Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to genera…