Update model card with description and paper link (#1)

- Update model card with description and paper link (fa6d40a365c5971c63b9f5c1594dc57432f75a94)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,13 +1,29 @@
 ---
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
 license: apache-2.0
 metrics:
 - mae
 - accuracy
-base_model:
-- Qwen/Qwen2.5-VL-7B-Instruct
 pipeline_tag: video-text-to-text
 ---
 
+# PRIMO R1: Process Reasoning Induced Monitoring
+
+This repository contains the model weights for PRIMO R1, introduced in the paper [From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation](https://huggingface.co/papers/2603.15600).
+
+## Model Description
+
+PRIMO R1 is a 7B framework designed to transform video Multimodal Large Language Models (MLLMs) from passive "Observers" into active "Critics" for long-horizon robotic manipulation. While traditional models often focus on recognizing ongoing events, PRIMO R1 evaluates the current state of a task relative to its final goal.
+
+The model is fine-tuned from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) using outcome-based Reinforcement Learning to elicit explicit Chain-of-Thought (CoT) generation for progress estimation. Its architecture incorporates a structured temporal input that anchors video sequences between the initial and current state images.
+
+## Key Features
+
+- **RL-Induced Reasoning**: Uses outcome-based RL to incentivize the generation of thought processes that evaluate state progress.
+- **State-of-the-Art Performance**: Achieves a 50% reduction in the mean absolute error of specialized reasoning baselines, outperforming much larger general MLLMs.
+- **Strong Generalization**: Exhibits zero-shot performance on failure detection tasks, achieving 67.0% accuracy on the RoboFail benchmark and surpassing closed-source models like OpenAI o1.
+- **Structured Temporal Input**: Explicitly anchors the video sequence between initial and current state images to provide clear goal-oriented context.
 
 ## Citations