---
library_name: pytorch
tags:
  - polymers
  - molecular-editing
  - polybert
  - supervised-learning
---

# PolyEdit DFT-consistent task augmentation

This repository contains the SFT policies used for the 369, 1,000, and
2,000-request PolyEdit task-augmentation comparison. Each condition has five
seed directories containing the policy, a self-contained policy bundle, the
corresponding fine-tuned polyBERT `Egc` verifier, and the run configuration.

## Training setup

- Property: DFT-labeled chain band gap, `Egc`
- Requests: 369 original, then 1,000 and 2,000 after train-only augmentation
- Decision steps: 1,263, 5,145, and 11,133
- Edit budget: at most three matched-pair transitions
- Policy loss: set-valued negative log-likelihood over valid next edits
- Seeds: 1004, 1005, 1006, 1007, 1008
- Evaluation: fixed held-out sources, 112 single-step and 36 multi-step requests

## Results

Five-seed means and 95% t confidence intervals:

| Requests | Single-target success | Multi-step success |
|---:|---:|---:|
| 369 | 0.889 ± 0.033 | 0.811 ± 0.089 |
| 2,000 | 0.941 ± 0.022 | 0.928 ± 0.019 |

Paired improvements were +0.052 for single-target success (`p=0.040`) and
+0.117 for multi-step success (`p=0.025`).

## Limitations

The policies were trained and evaluated only for `Egc` within the frozen
matched-pair graph. Success therefore measures navigation among polymers with
available DFT labels. It does not establish performance on arbitrary polymer
structures or other properties.