1 citations · 2 across the 6 of their papers we have counts for
6 papers
Reuse your FLOPs: Scaling RL on Hard Problems by Conditioning on Very Off-Policy Prefixes
Amrith Setlur, Zijian Wang, Andrew Cohen +2
Typical reinforcement learning (RL) methods for LLM reasoning waste compute on hard problems, where correct on-policy traces are rare, policy gradients vanish, and learning stalls.…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
PrefPalette: Personalized Preference Modeling with Latent Attributes
Shuyue Stella Li, Melanie Sclar, Hunter Lang +7
Personalizing AI systems requires understanding not just what users prefer, but the reasons that underlie those preferences - yet current preference models typically treat human ju…
reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs
Zhaofeng Wu, Michihiro Yasunaga, Andrew Cohen +3
Reward models have become a staple in modern NLP, serving as not only a scalable text evaluator, but also an indispensable component in many alignment recipes and inference-time al…
ALMA: Alignment with Minimal Annotation
Michihiro Yasunaga, Leonid Shamis, Chunting Zhou +4
Recent approaches to large language model (LLM) alignment typically require millions of human annotations or rely on external aligned models for synthetic data generation. This pap…
The ART of LLM Refinement: Ask, Refine, and Trust
Kumar Shridhar, Koustuv Sinha, Andrew Cohen +6
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations? A popular concept, referre…