4 papers
The Invisible Leash: Why RLVR May or May Not Escape Its Origin
Fang Wu, Weihao Xuan, Ximing Lu +4
Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising method for enhancing LLMs' capabilities. However, it remains unclear whether the curr…
BroRL: Scaling Reinforcement Learning via Broadened Exploration
Jian Hu, Mingjie Liu, Ximing Lu +8
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL has s…
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Sean Welleck, Amanda Bertsch, Matthew Finlayson +5
One of the most striking findings in modern research on large language models (LLMs) is that scaling up compute during training leads to better results. However, less attention has…
StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements
Jillian Fisher, Skyler Hallinan, Ximing Lu +3
Authorship obfuscation, rewriting a text to intentionally obscure the identity of the author, is an important but challenging task. Current methods using large language models (LLM…