9 papers
Tuning-free Visual Effect Transfer across Videos
Maxwell Jones, Rameen Abdal, Or Patashnik +4
We present RefVFX, a new framework that transfers complex temporal effects from a reference video onto a target video or image in a feed-forward manner. While existing methods exce…
POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration
Yuxiao Qu, Amrith Setlur, Virginia Smith +2
Reinforcement learning (RL) has improved the reasoning abilities of large language models (LLMs), yet state-of-the-art methods still fail to learn on many training problems. On har…
RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems
Yuxiao Qu, Anikait Singh, Yoonho Lee +4
Reasoning requires going beyond pattern matching or memorization of solutions to identify and implement "algorithmic procedures" that can be used to deduce answers to hard problems…
Rethinking Thinking Tokens: LLMs as Improvement Operators
Lovish Madaan, Aniket Didolkar, Suchin Gururangan +6
Reasoning training incentivizes LLMs to produce long chains of thought (long CoT), which among other things, allows them to explore solution strategies with self-checking. This res…
InSTA: Towards Internet-Scale Training For Agents
Brandon Trabucco, Gunnar Sigurdsson, Robinson Piramuthu +1
The predominant approach for training web navigation agents is to gather human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that hu…
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
Yuxiao Qu, Matthew Y. R. Yang, Amrith Setlur +4
Training models to effectively use test-time compute is crucial for improving the reasoning performance of LLMs. Current methods mostly do so via fine-tuning on search traces or ru…