metadata
datasets: wuc1/bi_so101_ffp_20260603_200349_subtask
library_name: lerobot
license: apache-2.0
model_name: sarm
pipeline_tag: robotics
tags:
- sarm
- reward-model
- lerobot
- robotics
Reward Model Card for sarm
A Success-Aware Reward Model (SARM) predicts a dense reward signal from observations, typically used downstream for reinforcement learning or human-in-the-loop fine-tuning when task success is not directly observable.
This reward model has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Reward Model
Train from scratch
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--reward_model.type=sarm \
--output_dir=outputs/train/<desired_reward_model_repo_id> \
--job_name=lerobot_reward_training \
--reward_model.device=cuda \
--reward_model.repo_id=${HF_USER}/<desired_reward_model_repo_id> \
--wandb.enable=true
Writes checkpoints to outputs/train/<desired_reward_model_repo_id>/checkpoints/.
Load the reward model in Python
from lerobot.rewards import make_reward_model
reward_model = make_reward_model(pretrained_path="<hf_user>/<reward_model_repo_id>")
reward = reward_model.compute_reward(batch)
Model Details
- License: apache-2.0