What chunk_size does to an ACT policy
A single-variable LeRobot ACT experiment on PushT: success falls from 14% to 8% to 0% as chunk_size grows from 16 to 32 to 100.
I trained ACT policies on the PushT task and changed one thing: chunk_size, the number of future actions the policy predicts at once. Each policy was evaluated on 50 episodes.
Results
| chunk_size | Success | Avg. max reward | Avg. total reward |
|---|---|---|---|
| 16 | 14% (7/50) | 0.686 | 71.29 |
| 32 | 8% (4/50) | 0.706 | 79.15 |
| 100 | 0% (0/50) | 0.326 | 28.28 |
Success counts an episode only when the T-block holds its coverage of the goal, so a brief graze doesn't count.
The finding
Success falls every time the chunk grows, but the best single moment peaks at 32. With 32-step chunks the policy approaches the goal more smoothly and then overshoots. It commits to 32 actions without looking again, and the drift builds up before the next re-plan. At 100 it re-plans only about three times an episode and never gets close.
The bottleneck is open-loop staleness, not the training horizon.
The full write-up, per-episode data and the steps to reproduce it are in the original post and the repo.