# 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