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README.md

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+---
+license: mit
+library_name: transformers
+tags:
+  - robotics
+  - reward-model
+  - video-language-model
+  - reasoning
+  - reinforcement-learning
+  - qwen3-vl
+  - bf16
+pipeline_tag: image-text-to-text
+datasets:
+  - Philip-MIT/sole_training_data
+---
+
+# 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 an image or video-frame montage containing the first, previous, and current timestep views, plus a task description and prior progress value, 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.
+
+## Intended Use
+
+SOLE-R1-8B is intended for:
+
+- Robotics reward prediction
+- Online robot RL reward generation
+- Evaluating task progress from robot videos
+- Interpretable video-language reasoning for manipulation tasks
+- Research on learned reward models and robotic foundation models
+
+It is not intended as a general-purpose safety-critical robotics controller. The model should be validated in the target environment before use in closed-loop robotic systems.
+
+## Quick Start
+
+The recommended interface for inference is [RoboReason](https://github.com/Philip-MIT/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)
+
+
+
+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](https://huggingface.co/Philip-MIT/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.
+