Long-MDR: Long-Context Reinforcement Learning for Multimodal Deep-Research Agents
The next generation of multimodal research agents must reason over long-lived research histories rather than short model completions. During a single task, an agent may repeatedly search the web, inspect visual evidence, revisit earlier hypotheses, and accumulate tens of thousands of tokens of multimodal context. Despite this trend, online RL for multimodal research agents remains largely confined to shorter contexts and interaction horizons. We push online RL training to 128k context and 75+ tool-interaction turns. To our knowledge, this is the first online multimodal deep-research RL study trained at 128k context, and the first trained with a 75 tool-turn horizon. Scaling to this regime...
| Authors | On Tai Tang |
|---|---|
| Primary category | cs.CV |
| Published | 4 October 2026 |
| Last updated at source | 9 October 2026 |
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