2 citations · 5 across the 9 of their papers we have counts for
10 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…
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…
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…
Understanding Visual Concepts Across Models
Brandon Trabucco, Max Gurinas, Kyle Doherty +1
Large multimodal models such as Stable Diffusion can generate, detect, and classify new visual concepts after fine-tuning just a single word embedding. Do models learn similar word…
Leafy Spurge Dataset: Real-world Weed Classification Within Aerial Drone Imagery
Kyle Doherty, Max Gurinas, Erik Samsoe +5
Invasive plant species are detrimental to the ecology of both agricultural and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that has spread through much of N…
Stylus: Automatic Adapter Selection for Diffusion Models
Michael Luo, Justin Wong, Brandon Trabucco +5
Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters…
Automatic Question-Answer Generation for Long-Tail Knowledge
Rohan Kumar, Youngmin Kim, Sunitha Ravi +4
Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering question…