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:

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<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 RewardGen:

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# pip install -U rewardgen

from rewardgen import generate, video_plot

# test_videos provided at the github repo: https://github.com/Philip-MIT/rewardgen
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 = 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]}
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:

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from rewardgen.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:

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Pick up the cube from the table.

Output Format

The expected output format is:

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

Example:

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<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.

It also includes a diverse collection of general spatial and multi-frame temporal reasoning data (e.g., from SSR-CoT, SpatialVLM, Spot-the-diff, Embodied CoT, RoboVQA, Robo2VLM-Reasoning) to serve as a foundational layer of our training mixture.

The full dataset is approximately 2TB.

Streaming example:

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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:

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@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.