3 papers
cs.CL2026
Mitigating Exploration Bias in RL for Multi-Instruction Following
Mian Zhang, Yueqin Yin, Kaiyu He +4
RL has emerged as a powerful paradigm for enhancing the instruction following capabilities of LLMs. While existing training recipes achieve substantial gains, we find that they suf…
cs.LG2025
KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
Zhangchen Xu, Yang Liu, Yueqin Yin +2
We introduce KodCode, a synthetic dataset that addresses the persistent challenge of acquiring high-quality, verifiable training data across diverse difficulties and domains for tr…
cs.CL2025
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
Yueqin Yin, Shentao Yang, Yujia Xie +5
Reinforcement learning from human feedback (RLHF) has been widely adopted to align language models (LMs) with human preference. Prior RLHF works typically take a bandit formulation…