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README.md

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-license: apache-2.0
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+# STOP-1.5B: Early Path Pruning Module
+
+This repository contains the STOP module trained for prefix-level path pruning on top of a 1.5B reasoning model.
+
+## Overview
+
+STOP (Super TOken for Pruning) is a lightweight module that predicts whether a reasoning prefix is promising, enabling early pruning of unproductive paths.
+
+It operates by:
+
+- Appending a special `[STOP]` token
+- Reading internal KV-cache states
+- Producing a scalar quality score
+
+## Architecture
+
+- Base model: frozen reasoning model (1.5B)
+- Adapter: LoRA-based critique module
+- Head: lightweight classifier
+
+## Training
+
+The model is trained using prefix–potential supervision constructed via Monte Carlo rollouts.
+
+## Usage
+
+After generating prefixes, STOP can be used to:
+
+1. Score each prefix
+2. Select top-k candidates
+3. Resume generation only on selected paths
+
+## Results
+
+- Significant token reduction (up to 70%)
+- Improved reasoning accuracy
+- Strong performance in tool-use settings (AIMO3)
+
+## Citation