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cs.LG2026
ExpRL: Exploratory RL for LLM Mid-Training
Violet Xiang, Amrith Setlur, Chase Blagden +2
Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but its success depends critically on the coverage present in the base model. In p…
cs.LG2026
Data-efficient pre-training by scaling synthetic megadocs
Konwoo Kim, Suhas Kotha, Yejin Choi +3
Synthetic data augmentation has emerged as a promising solution when pre-training is constrained by data rather than compute. We study how to design synthetic data algorithms that…
cs.CL2026
One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models
Daniel Fein, Max Lamparth, Violet Xiang +2
Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences. However, RM-based preference-tuning is vulnerable to reward hacking, whereby L…