Loss is applied to all assistant turns in the multi-turn trajectory, enabling the model to learn observation grounding, action selection, tool use, and recovery from errors.

Training data used in this run is ALFWorld only (see datasets in YAML). Evaluation in the competition includes AgentBench tasks (DBBench + ALFWorld) by the organizers.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: LoRA (PEFT)
Max sequence length: 2048
Epochs: 2
Learning rate: 1.5e-6
LoRA: r=64, alpha=128

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "KOUJI039/structeval-qwen3-4b-sft-try20"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Training data:

u-10bei/sft_alfworld_trajectory_dataset_v5

This repository does NOT redistribute the dataset. Users must comply with the dataset license and base model terms.