File size: 989 Bytes
cef443c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # 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
|