Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes

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Jing Tan, Zhaoyang Zhang, Yantao Shen, Jiarui Cai, Shuo Yang, Jiajun Wu, Wei Xia, Zhuowen Tu, Stefano Soatto

This repository contains training scripts for Talk2Move, scene-level image editing models using GRPO (Group Relative Policy Optimization).

In this work, we demonstrate that RLVR can effectively improve prompt-following performance for the corresponding tasks in vision-related settings, and we propose an early stopping strategy that greatly improves the sampling efficiency of flow-based GRPO.

Licenses

This codebase is build upon:

• Flow-GRPO, which is licensed under MIT license;
• Orient-Anything that is licensed under CC-BY-4.0 license,
• lang-segment-anything that is licensed underApache-2.0 license;
• Grounding-DINO that is licensed under Apache-2.0 license

Key Modifications

Added an object-manipulation reward suite for editing tasks

Modified file: talk2move/rewards.py

• Added new editing-focused rewards: translation, ours_qwenvl (zero-shot qwenvl scorer), ours_clip, rotation, resize, lpips
• Extended multi_score to support editing-task inputs via a new 4-argument path: images, ref_images, prompts, metadata.

Upgraded the GRPO sampling pipeline from pure SDE to SDE + shortcut ODE

Modified files: grpo/diffusers_patch/qwenimage_edit_pipeline_with_logprob.py, grpo/diffusers_patch/sd3_sde_with_logprob.py

• Introduced ode_shortcut_step in qwenimage_edit_pipeline_with_logprob.py, extending sampling from pure SDE to SDE + shortcut ODE.
• Added ode_shortcut_step in sd3_sde_with_logprob.py, which updates latents using continuous-time steps (t -> t_prev) and dt (instead of the scheduler’s discrete step+1), and performs deterministic ODE updates without injecting random noise.

Setup

Prerequisites

• Python 3.8+
• PyTorch with CUDA support
• 16 GPUs (2 nodes × 8 GPUs per node)
• Required Python packages (install via pip install -e .)

Configuration

Before running training, update the paths in your configuration:

1. Replace enter_path_here placeholders in the codebase with your actual paths
2. Update MASTER_ADDR in scripts/multi_node/qwenimagedit/main.sh to match your master node IP
3. Ensure all nodes can communicate via the specified master address and port

The training script uses the following default settings:

• GPUs per node: 8
• Number of nodes: 2
• Total GPUs: 16
• Master port: 19001
• Config: config/grpo.py:talk2move

To modify these settings, edit scripts/multi_node/qwenimagedit/main.sh.

Available Configurations

Check config_files/grpo.py for available training configurations:

Qwen-Image-Edit Configurations

• Various task-specific configs for rotation(talk2move_rotation), resize (talk2move_resize), translation (talk2move_translation)

Each configuration specifies:

• Model architecture and checkpoint paths
• Batch sizes and gradient accumulation steps
• Sampling parameters (num_steps, guidance_scale)
• Reward function weights
• Training hyperparameters (learning rate, beta, etc.)

Running Training (16 GPUs)

To run training on 16 GPUs across 2 nodes (8 GPUs per node):

On Node 0 (Master):

sh scripts/multi_node/qwenimagedit/main.sh 0

On Node 1 (Worker):

sh scripts/multi_node/qwenimagedit/main.sh 1

Troubleshooting

• Connection issues: Verify that MASTER_ADDR is correct and nodes can communicate
• CUDA out of memory: Reduce batch size in the config file
• Path errors: Ensure all enter_path_here placeholders are replaced with valid paths
• Reward server errors: Check that reward server IPs (your-api-server-ip, your-reward-server-ip) are correctly configured
• Import errors: Run pip install -e . to install the package in development mode
• NCCL timeout: Increase timeout or check network connectivity between nodes

Citation

If you use this code in your research, please cite the relevant papers for the models and methods use

@misc{tan2026talk2movereinforcementlearningtextinstructed,
      title={Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes}, 
      author={Jing Tan and Zhaoyang Zhang and Yantao Shen and Jiarui Cai and Shuo Yang and Jiajun Wu and Wei Xia and Zhuowen Tu and Stefano Soatto},
      year={2026},
      eprint={2601.02356},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.02356}, 
}

Contribution

This codebase is built by Jing Tan during her internship at AWS Agentic AI.

For any question, feel free to contact her via

tj023@ie.cuhk.edu.hk