SOLE-R1-8B

SOLE-R1-8B is a video-language reward reasoning model for robotics. It is designed to estimate task progress from robot video frames and a natural-language task description, producing both per-timestep reasoning traces and scalar progress predictions that can be used as rewards for online robot reinforcement learning.

This model accompanies the paper “SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot RL” by Philip Schroeder, Thomas Weng, Karl Schmeckpeper, Eric Rosen, Stephen Hart, and Ondrej Biza.

• Paper: https://arxiv.org/abs/2603.28730
• Project page: https://philip-mit.github.io/sole-r1/
• Code: https://github.com/Philip-MIT/sole-r1-model
• Training data: https://huggingface.co/datasets/Philip-MIT/sole_training_data

Model Description

SOLE-R1 predicts robot task progress from visual observations. Given a video and a task description, the model outputs a reasoning trace and a scalar progress estimate.

Expected output format:

<think>reasoning about task progress</think><answer>progress%</answer>

The progress estimate is intended to serve as a dense reward signal for robotic reinforcement learning, especially when manually engineered rewards are unavailable.

Quick Start

The recommended interface for inference is RoboReason:

# pip install -U roboreason

import roboreason as rr

video_paths = [
    "test_videos/robosuite/lift/unsuccessful/robosuite_lift_episode_12_unsuccessful_max_reward_38.mp4"
]

task_description = "Pick up the cube from the table."

rewards, reasoning_traces = rr.generate(
    model="SOLE-R1",
    task_description=task_description,
    video_paths=video_paths,
    view_type_per_video=["external and wrist"],
    verbose=False,
)
print(rewards)
print(reasoning_traces)

# Plotting with show_reasoning_traces=True
output_sole = {"model": "SOLE-R1", "rewards": rewards[0], "reasoning_traces": reasoning_traces[0]}
rr.video_plot(
    outputs=[output_sole], 
    plot_save_path='model_outputs/sole-r1/robosuite/lift/unsuccessful/robosuite_lift_episode_12_unsuccessful_max_reward_38.mp4', 
    video_path=video_paths[0],
    show_reasoning_traces=True,
    task_description=task_description,
    verbose=False
)

Optional pre-download:

from roboreason.utils.model_utils import get_model_dir

get_model_dir("sole-r1")

Input Format

The model is trained to reason over robot task progress using prompts that include:

• A robot task description
• The first timestep progress, typically 0%
• The previous timestep progress
• Visual observations from the first, previous, and current timesteps
• Multiple camera views when available, such as external and wrist cameras

Example task description:

Pick up the cube from the table.

Output Format

The expected output format is:

<think>[reasoning about visual task progress]</think><answer>[current task progress]%</answer>

Example:

<think>The gripper has moved closer to the cube but has not yet grasped or lifted it. This indicates incremental progress from the previous timestep.</think><answer>22%</answer>

Downstream systems should parse the numeric value inside <answer>...</answer> as the reward/progress estimate.

Training Data

The model was trained on the SOLE-R1-8B training dataset.

The dataset contains robot task progress examples with images, prompts, reasoning completions, and progress labels. The full dataset is approximately 2TB.

Streaming example:

from datasets import load_dataset

ds = load_dataset(
    "Philip-MIT/sole_training_data",
    split="train",
    streaming=True,
)

for row in ds:
    print(row)
    break

Citation

BibTeX:

@misc{schroeder2026soler1,
  title={SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot RL},
  author={Philip Schroeder and Thomas Weng and Karl Schmeckpeper and Eric Rosen and Stephen Hart and Ondrej Biza},
  year={2026},
  eprint={2603.28730},
  archivePrefix={arXiv},
  primaryClass={cs.RO}
}

License

This repository is released under the MIT License.