pith:OO2D3RD6
VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
Reinforcement fine-tuning with rule-based temporal rewards creates a video model with state-of-the-art spatio-temporal perception.
arxiv:2504.06958 v5 · 2025-04-09 · cs.CV
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Claims
Through joint RFT on multiple spatio-temporal perception tasks, we developed VideoChat-R1, a powerful Video MLLM. VideoChat-R1 achieves state-of-the-art spatio-temporal perception, demonstrating significant improvements in tasks like temporal grounding (+31.8) and object tracking (+31.2), while also improving general QA benchmarks.
That carefully designed rule-based rewards focused on temporal associations will produce generalizable improvements in video reasoning without post-hoc tuning or hidden data selection that inflates the reported deltas.
Reinforcement fine-tuning with temporal rewards produces VideoChat-R1, a video MLLM showing large gains on spatio-temporal perception benchmarks such as +31.8 temporal grounding and +31.2 object tracking.
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| First computed | 2026-05-17T23:38:50.192174Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
73b43dc47eb9c8099fadc7474deb4a195144c4997a545220f336e76bc37dbcc5
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Canonical record JSON
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