from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("michalsr/toolmerge-planner-grpo")
model = AutoModelForCausalLM.from_pretrained(
    "michalsr/toolmerge-planner-grpo",
    torch_dtype="bfloat16",
)

To use inside ToolMerge, override the planner checkpoint at the CLI:

bash

toolmerge config=configs/m2m/qwen3_8.yaml \
    model.base=michalsr/toolmerge-planner-grpo

Training recipe

Setting	Value
Base model	`Qwen/Qwen3-VL-8B-Instruct`
Reward	`frames_in_gt=1.0`, `consistency=1.0`
Training data	`train_correct_uniform_8f_clip_max1.json` (filtered M2M train split, ~1500 items)
Optimizer	`paged_adamw_8bit`, lr=1e-6, bf16
Compute	2 nodes × 4 GPUs
Step	`global_step=50`
Framework	TRL 0.27.2, transformers 4.57.6, PyTorch 2.10.0

Full training config: training/configs/m2m_grpo.yaml in the ToolMerge repo.

Citation

bibtex

@misc{shlapentokhrothman2026decomposingqueriestoolcalls,
  title         = {Decomposing Queries into Tool Calls for Long-Video Keyframe Retrieval},
  author        = {Michal Shlapentokh-Rothman and Prachi Garg and Yu-Xiong Wang and Derek Hoiem},
  year          = {2026},
  eprint        = {2605.23826},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2605.23826},
}

Cite the GRPO method:

bibtex

@article{shao2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

Code repo: https://github.com/michalsr/ToolMerge.