ATOD_ckpt

ATOD Teacher Checkpoints

Teacher checkpoints for ATOD (Annealed Turn-aware On-policy Distillation), a hybrid online distillation algorithm that combines OPD and GRPO with a smoothly annealed schedule and turn-level disagreement-uncertainty reweighting (T-DUR) for training small language-model agents on long-horizon, multi-turn tasks.

• 📄 Paper: arXiv:2606.27814
• 💻 Code: ATOD repository

Every model here is a Qwen3 policy trained with GRPO in its target agentic environment, and is used as the teacher during ATOD distillation of Qwen3-0.6B / 1.7B / 4B students.

Contents

Each subfolder contains a standard transformers / vLLM-loadable model directory under actor_hf/ (weights in safetensors, plus tokenizer and config files).

Download

Download one checkpoint (recommended — the 30B-A3B models are large):

pip install -U "huggingface_hub[cli]"

hf download TanQT24/ATOD_ckpt \
  --include "alfworld_grpo_qwen3_4b/*" \
  --local-dir ~/ckpts/ATOD_ckpt

Download everything:

hf download TanQT24/ATOD_ckpt --local-dir ~/ckpts/ATOD_ckpt

Python API:

from huggingface_hub import snapshot_download

path = snapshot_download(
    repo_id="TanQT24/ATOD_ckpt",
    allow_patterns=["search_grpo_qwen3_4b/*"],
    local_dir="~/ckpts/ATOD_ckpt",
)

Usage

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "TanQT24/ATOD_ckpt"
sub = "alfworld_grpo_qwen3_4b/actor_hf"

tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=sub)
model = AutoModelForCausalLM.from_pretrained(
    repo, subfolder=sub, torch_dtype="bfloat16", device_map="auto"
)

vLLM

vllm serve ~/ckpts/ATOD_ckpt/alfworld_grpo_qwen3_4b/actor_hf \
  --served-model-name atod-teacher-alfworld-4b

As an ATOD teacher

In the ATOD repo, point teacher_model_path in examples/atod_trainer/*.sh at the downloaded actor_hf directory:

student_model_path=Qwen/Qwen3-1.7B
teacher_model_path=~/ckpts/ATOD_ckpt/alfworld_grpo_qwen3_4b/actor_hf

Using these checkpoints lets you skip teacher GRPO training and run ATOD distillation directly.

Notes

• Models are task-specific agents: use the ALFWorld teacher for ALFWorld, the WebShop teacher for WebShop, and the Search teacher for Search-QA. They expect the prompt / action formats produced by the environments in the ATOD repo.
• Checkpoint names encode the environment and the base model, e.g. webshop_grpo_qwen3_4b is the Qwen3-4B GRPO teacher for WebShop.

Citation

@misc{atod2026,
      title={ATOD: Annealed Turn-Aware On-Policy Distillation for Multi-Turn Agentic Tasks},
      year={2026},
      eprint={2606.27814},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2606.27814},
}