Parameter	Value
`steps`	`15000`
`batch_size`	`8`
`eval_freq`	`0`
`save_freq`	`3000`
`num_workers`	`4`
`seed`	`3`
`eval.n_episodes`	`1`
`eval.batch_size`	`1`
`eval.use_async_envs`	`True`

📐 Policy Architecture

All defaults — no overrides applied.

🎯 Eval Config

Parameter	Value
`env.type`	`libero`
`env.task`	`libero_goal`
`eval.n_episodes`	`1`
`eval.batch_size`	`4`
`eval.use_async_envs`	`False`
`policy.path`	`/kaggle/working/outputs/train/libero_goal_seed3/checkpoints/last/pretrained_model`

📊 Eval Results

Metric	Value
Episodes	`10`
Success rate	`0.0%`
Avg sum reward	`0.00`
Avg max reward	`0.00`
Eval time (s)	`1355.6`

Citation

bibtex

@misc{cadene2024lerobot,
  author = {Cadene, Remi and Alibert, Simon and others},
  title  = {LeRobot},
  year   = {2024},
  url    = {https://github.com/huggingface/lerobot}
}

iFaz/diffusion-libero_goal-seed3-15000

DIFFUSION Policy — diffusion_libero_goal_seed3

📦 Dataset

🏋️ Training Config

📐 Policy Architecture

🎯 Eval Config

📊 Eval Results

Citation

iFaz/
diffusion-libero_goal-seed3-15000