989 citations · 6.1k across the 184 of their papers we have counts for
9 papers · 1 filter
Unsupervised Multimodal Reward Modeling
Yueying Li, Jingxuan Fan, Kianté Brantley +2
Vision-language reward models are trained on annotated preference pairs, which are expensive to collect, can disagree, and are ranked by benchmark accuracy. We build one with no hu…
When Do Biological Reasoning Models Use Their Biological Inputs?
Ada Fang, Nikitha Thoduguli, Lukas Fesser +3
Biological reasoning models use post-training to connect LLMs to biological foundation model representations and biological text. Their benchmark accuracy is taken as evidence that…
Blackboard Intelligence Can Surpass Autoregressive on Globally Constrained Problems
Woosang Jeon, Jaeyeon Kim, Sham Kakade +6
Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on problems governed by complex global…
Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models
Samy Jelassi, Mujin Kwun, Rosie Zhao +5
Cross-entropy (CE) training provides dense and scalable supervision for language models, but it optimizes next-token prediction under teacher forcing rather than sequence-level beh…
Next-token Preference: Scaling Unsupervised Reward Modeling
Jingxuan Fan, Yueying Li, Zhenting Qi +4
Learning from feedback advances the capabilities and safety of frontier models, but preference supervision remains costly and difficult to scale. We introduce next-token preference…
Prescriptive Scaling Reveals the Evolution of Language Model Capabilities
Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis +1
Machine learning model performance improvements tend to arise from competition and application. For deployment, we consider prescriptive scaling laws: given a pre-training compute…