Ego-R1, a reinforcement learning-based framework, uses a structured tool-augmented chain-of-thought process to reason over ultra-long egocentric videos, achieving better performance than existing methods by extending time coverage to a week.
We introduce Ego-R1, a novel framework for reasoning over ultra-long (i.e.,
in days and weeks) egocentric videos, which leverages a structured
Chain-of-Tool-Thought (CoTT) process, orchestrated by an Ego-R1 Agent trained
via reinforcement learning (RL). Inspired by human problem-solving strategies,
CoTT decomposes complex reasoning into modular steps, with the RL agent
invoking specific tools, one per step, to iteratively and collaboratively
answer sub-questions tackling such tasks as temporal retrieval and multi-modal
understanding. We design a two-stage training paradigm involving supervised
finetuning (SFT) of a pretrained language model using CoTT data and RL to
enable our agent to dynamically propose step-by-step tools for long-range
reasoning. To facilitate training, we construct a dataset called Ego-R1 Data,
which consists of Ego-CoTT-25K for SFT and Ego-QA-4.4K for RL. Furthermore, our
Ego-R1 agent is evaluated on a newly curated week-long video QA benchmark,
Ego-R1 Bench, which contains human-verified QA pairs from hybrid sources.
Extensive results demonstrate that the dynamic, tool-augmented chain-of-thought
reasoning by our Ego-R1 Agent can effectively tackle the unique challenges of
understanding ultra-long egocentric videos, significantly extending the time
coverage from few hours to a week.
