README.md

---
base_model: TIGER-Lab/BrowserAgent-RFT
library_name: peft
tags:
  - lora
  - web-agent
  - reinforcement-learning
  - self-play
---

# BrowserAgent-RFT web co-evolution executors

LoRA adapters (r=16, α=32) over **TIGER-Lab/BrowserAgent-RFT** (Qwen2.5-7B), produced by an
Agent0-style web co-evolution loop: a curriculum LoRA proposes `<PAGE>/<GOAL>` tasks and this
executor LoRA solves them multi-turn inside a frozen WebWorld world model, trained with GRPO
using a label-free self-consistency reward.

## Adapters

| subfolder | iteration |
|-----------|-----------|
| `exec_v1` | co-evolution iter 1 |
| `exec_v2` | co-evolution iter 2 |
| `exec_v3` | co-evolution iter 3 (final) |

## Result (held-out hard-20, 5-seed, strict `final_click`)

```
base 0.640  →  exec_v1 0.740  →  exec_v2 0.790  →  exec_v3 0.880   (+0.24)
```

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("TIGER-Lab/BrowserAgent-RFT")
tok  = AutoTokenizer.from_pretrained("TIGER-Lab/BrowserAgent-RFT")
model = PeftModel.from_pretrained(base, "Sarim-Hash/browseragent-rft-web-coevo-executors", subfolder="exec_v3")
```

These are adapters only — the base model `TIGER-Lab/BrowserAgent-RFT` is required.