GROW decomposes trajectories into state-action samples to enable GRPO for multi-turn VLM agents and reports state-of-the-art results on more than 800 Minecraft tasks.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
IntentVLA conditions VLA chunk generation on a compact intent code from recent observations and introduces AliasBench to evaluate stability under short-horizon observation aliasing, reporting gains on multiple robot benchmarks.
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
citing papers explorer
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GROW: Aligning GRPO with State-Action Modeling for Open-World VLM Agents
GROW decomposes trajectories into state-action samples to enable GRPO for multi-turn VLM agents and reports state-of-the-art results on more than 800 Minecraft tasks.
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IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation
IntentVLA conditions VLA chunk generation on a compact intent code from recent observations and introduces AliasBench to evaluate stability under short-horizon observation aliasing, reporting gains on multiple robot benchmarks.
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Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.