Question
The recent π*₀.₆ announcement introduced RECAP, a reinforcement learning method that significantly improves performance over supervised learning. However, the current codebase only includes supervised learning implementations.
Are there plans to open-source the RECAP training code? If so, what is the expected timeline?
Missing Components
The current scripts/train.py implements supervised learning but lacks:
- Value function training
- Advantage computation
- Advantage-conditioned policy architecture
- Online RL training loop
- Human correction integration
Context
RECAP achieves impressive results:
- 2x+ throughput improvement on complex tasks
- ~50% reduction in failure rates
- Ability to learn from experience and corrections
This would be extremely valuable for the robotics community working on real-world deployments.
Request
Could the team provide:
- Timeline for RECAP code release (if planned)
- π₀.₆ base model checkpoint
- Guidance for community implementation (if official release is not immediate)
Thank you for the excellent work on OpenPI!
Question
The recent π*₀.₆ announcement introduced RECAP, a reinforcement learning method that significantly improves performance over supervised learning. However, the current codebase only includes supervised learning implementations.
Are there plans to open-source the RECAP training code? If so, what is the expected timeline?
Missing Components
The current
scripts/train.pyimplements supervised learning but lacks:Context
RECAP achieves impressive results:
This would be extremely valuable for the robotics community working on real-world deployments.
Request
Could the team provide:
Thank you for the excellent work on OpenPI!