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
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.
- Downloads last month
- -