Reliable VLA Fine-Tuning & Evaluation
4-GPU SmolVLA training and RNG-controlled LIBERO evaluation
I built an end-to-end SmolVLA pipeline that validates the native LIBERO data contract, trains with 4-GPU DDP, evaluates through the official closed-loop protocol, and makes stochastic checkpoint comparisons attributable.
Impact
- 3.82× DDP speedup on 4×RTX 4090 at matched per-GPU batch
- 1,693 episodes / 273,465 frames / 40 LIBERO tasks
- Identified a flow-matching RNG confound that changed the apparent direction of checkpoint scaling
- Rebuilt the comparison with identical environment starts and per-episode policy-noise streams
The controlled 5K→20K result was 2/30→4/30. I report this as a weak positive signal rather than a high-performing-policy claim; the main contribution is a training and evaluation workflow whose conclusions can be trusted.