Task adaptation of Vision-Language-Action model: 1st Place Solution for the 2025 BEHAVIOR Challenge
We present a vision-action policy that won 1st place in the 2025 BEHAVIOR Challenge - a large-scale benchmark featuring 50 diverse long-horizon household tasks in photo-realistic simulation, requiring bimanual manipulation, navigation, and context-aware decision making.
  Building on the Pi0.5 architecture, we introduce several innovations. Our primary contribution is correlated noise for flow matching, which improves training efficiency and enables correlation-aware inpainting for smooth action sequences. We also apply learnable mixed-layer attention and System 2 stage tracking for ambiguity resolution. Training employs multi-sample flow matching to reduce variance, while inference uses action compression and challenge-specific correction rules.
  Our approach achieves 26% q-score across all 50 tasks on both public and private leaderboards.
6937909419d912300c34a265
Ilia Larchenko
claimed_verified
2025-12-09T11:47:02.189Z
61e188e4ad060b40ab27de05
https://cdn-avatars.huggingface.co/v1/production/uploads/61e188e4ad060b40ab27de05/74Nz_9y-RyhRR3yvjpqAr.jpeg
Ilia Larchenko
IliaLarchenko
user
IliaLarchenko
6937909419d912300c34a266
Gleb Zarin
6937909419d912300c34a267
Akash Karnatak
Task adaptation of Vision-Language-Action model: 1st Place Solution for the 2025 BEHAVIOR Challenge
We present a vision-action policy that won 1st place in the 2025 BEHAVIOR Challenge - a large-scale benchmark featuring 50 diverse long-horizon household tasks in photo-realistic simulation, requiring bimanual manipulation, navigation, and context-aware decision making.
  Building on the Pi0.5 architecture, we introduce several innovations. Our primary contribution is correlated noise for flow matching, which improves training efficiency and enables correlation-aware inpainting for smooth action sequences. We also apply learnable mixed-layer attention and System 2 stage tracking for ambiguity resolution. Training employs multi-sample flow matching to reduce variance, while inference uses action compression and challenge-specific correction rules.
  Our approach achieves 26% q-score across all 50 tasks on both public and private leaderboards.
A vision-action policy using correlated noise for flow matching and learnable mixed-layer attention wins the 2025 BEHAVIOR Challenge with high performance across diverse household tasks.
flow matching correlated noise Pi0.5 architecture correlation-aware inpainting learnable mixed-layer attention System 2 stage tracking multi-sample flow matching action compression q-score