Deep Dive into LeHome Challenge RL Solution for Vision-Language-Action Models
philfung · x · 2026-08-07
Ilia Larchenko released part 2 of his deep dive into the LeHome Challenge solution (🥇 sim round, 🥈 real-robot final at ICRA 2026).
This installment focuses on the core details of the Reinforcement Learning (RL) solution:
- Reward engineering: Designing effective reward functions
- Advantage computation: Methods for optimizing policy evaluation
- Inference-time optimization: Techniques to boost actual model performance
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