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# Edit Flows
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> **Reference**
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> 📄 Paper: [Edit Flows: Flow Matching with Edit Operations](https://arxiv.org/abs/2506.09018)
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This directory provides an educational reference for training EditFlow models. It demonstrates how to adapt open-weight DLLMs—such as [LLaDA](https://arxiv.org/abs/2502.09992) and [Dream](https://arxiv.org/abs/2508.15487)—to support *insertion*, *deletion*, beyond the standard *substitution*(`mask`->`tokens`) operations. It also includes examples for training (pretraining and finetuning) EditFlow models from scratch.
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> [!NOTE]
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> - Examples are available for both LLaDA and Dream, but this README focuses on adapting open-weight LLaDA for edit operations ([`adapt_llada.py`](/examples/editflow/adapt_llada.py)) and reusing its architecture for training from scratch ([`pt_llada.py`](/examples/editflow/pt_llada.py) -> [`sft_llada.py`](/examples/editflow/sft_llada.py)).
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> - While `EditFlowCollator` supports custom `x0`, this README uses a fixed-length (128) masks as `x0`. The trained model generates text by replacing masks, deleting redundant ones, and inserting tokens as needed. To change the default `x0` distribution (e.g., empty sequences for [OneFlow](https://arxiv.org/abs/2510.03506)-like insertion-only generation), pass `--x0_sampler "empty"`.
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## Table of Contents
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- [Setup](#setup)
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- [Files overview](#files-overview)
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- [Training](#training)
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- [Adapting LLaDA-8B-Instruct to support insertion and deletion](#adapting-llada-8b-instruct-to-support-insertion-and-deletion)
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- [Pretraining & Finetuning from scratch](#pretraining--finetuning-from-scratch)
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- [Sampling](#sampling)
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- [Acknowledgement](#acknowledgement)
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## Setup
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> [!IMPORTANT]
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> **Slurm users:** Update `scripts/train.slurm.sh` and `mkdir logps`: see [(optional) Slurm setup](/README.md/#optional-slurm-setup) for details.
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## Files overview
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```
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dllm/pipelines/editflow
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├── __init__.py # Package initialization
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├── models
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│ ├── dream
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│ │ └── modelling_dream.py # EditFlowDream: architecture based on Dream
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│ └── llada
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│ └── modelling_llada.py # EditFlowLLaDA: architecture based on LLaDA
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├── trainer.py
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└── utils.py
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# example entry point for training / sampling
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examples/editflow
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├── adapt_dream.py # Example of adapting Dream for EditFlow directly
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├── adapt_llada.py # Example of adapting LLaDA for EditFlow directly
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├── generate.py # Generation example
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├── pt_dream.py # EditFlowDream pretraining example
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├── pt_llada.py # EditFlowLLaDA pretraining example
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├── pt.py # Pretraining function
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├── README.md # Documentation (you are here)
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├── sft_dream.py # EditFlowDream SFT example
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├── sft_llada.py # EditFlowLLaDA SFT example
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└── sft.py # Supervised finetuning function
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```
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## Training
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### Adapting [LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct) to support *insertion* and *deletion*
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The original LLaDA model generated text by iteratively substituting the given `<mask>` tokens to real tokens.
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<p align="center">
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<img src="https://github.com/ML-GSAI/LLaDA/blob/main/imgs/example_gradio.gif" alt="LLaDA demo" width="80%">
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</p>
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<p align="center"><em>Figure: Example Gradio demo for LLaDA.</em></p>
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However, LLaDA supports only substitution. This example shows how to adapt it so that, during decoding, the model can not only replace fixed-length masks (e.g., 128 tokens) with real text but also insert new tokens and delete unnecessary masks adaptively:
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```shell
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accelerate launch \
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--config_file scripts/accelerate_configs/zero2.yaml \
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examples/editflow/adapt_llada.py \
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--model_name_or_path "GSAI-ML/LLaDA-8B-Instruct" \
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--lm_head_key "model.transformer.ff_out" \
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--init_editflow_from_src True \
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--dataset_args "allenai/tulu-3-sft-mixture" \
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--output_dir "models/EditFlow-LLaDA-8B-Instruct-Adapt/tulu-3-sft-mixture" \
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--x0_sampler "masks[length:128]" \
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--max_length 1024 \
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--num_train_epochs 4 \
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--learning_rate 5e-5
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```
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If you are using slurm and want to train across, for example, four nodes (32 GPUs total), run:
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```shell
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sbatch --nodes=4 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "fsdp" \
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--script_path "examples/editflow/adapt_llada.py" \
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--model_name_or_path "GSAI-ML/LLaDA-8B-Instruct" \
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--lm_head_key "model.transformer.ff_out" \
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--init_editflow_from_src True \
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--dataset_args "allenai/tulu-3-sft-mixture" \
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--output_dir "models/EditFlow-LLaDA-8B-Instruct-Adapt/tulu-3-sft-mixture" \
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--x0_sampler "masks[length:128]" \
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--max_length 1024 \
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--num_train_epochs 4 \
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--learning_rate 5e-5
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```
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After training, you can use the [generate.py](/examples/editflow/generate.py) scripts to provide a visualized decoding trace to see how the model performs *insertion* and *deletion* beyond regular mask *substitutions*. See [Sampling](#sampling) for details.
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### Pretraining & Finetuning from scratch
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You can also train an EditFlow model from scratch (pretrain → SFT) without adapting an existing DLLM.
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Pretrain on a subset of [mlfoundations/dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) using 192 GPUs (24x8) and FSDP:
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```shell
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sbatch --nodes=24 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "fsdp" \
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--script_path "examples/editflow/pt_llada.py" \
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--model_name_or_path "GSAI-ML/LLaDA-8B-Base" \
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--dataset_args "mlfoundations/dclm-baseline-1.0" \
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--output_dir "models/EditFlow-LLaDA-8B-Base/dclm-baseline-1.0" \
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--x0_sampler "masks[length:128]" \
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--max_length 1024 \
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--max_steps 2000 \
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--learning_rate 3e-4
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```
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Finetune on a subset of [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) using 8 GPUS and FSDP for better instruction following:
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```shell
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# you can also run locally with `accelerate ...`
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sbatch --nodes=1 --gres=gpu:8 scripts/train.slurm.sh \
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--accelerate_config "fsdp" \
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--script_path "examples/editflow/sft_llada.py" \
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--model_name_or_path "models/EditFlow-LLaDA-8B-Base/dclm-baseline-1.0/checkpoint-final" \
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--dataset_args "allenai/tulu-3-sft-mixture[train:10000,test:1000]" \
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--output_dir "models/EditFlow-LLaDA-8B-Base/dclm-baseline-1.0" \
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--x0_sampler "masks[length:128]" \
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--max_length 1024 \
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--num_train_epochs 4 \
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--learning_rate 5e-5
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```
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## Sampling
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After training, you can visualize how the model performs mask substitution, insertion, and deletion during generation with [generate.py](/examples/editflow/generate.py). Inserted tokens appear <span style="color:blue; font-weight:bold">blue</span>, and tokens substituted from `<mask>` appear <span style="color:black; font-weight:bold">black</span>, and deleted tokens are shown with a strikethrough before they disappear.
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```shell
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# Generate a long sequence to visualize insertions after 128 <mask> tokens
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python examples/editflow/generate.py \
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--model_name_or_path "models/EditFlow-LLaDA-8B-Instruct-Adapt/tulu-3-sft-mixture/checkpoint-final" \
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--tau 0.02 --mask_length 128 --seed 7070 \
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--prompt "write a romantic story" --make_gif
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# Generate a short sequence to visualize deletions after 128 <mask> tokens
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python examples/editflow/generate.py \
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--model_name_or_path "models/EditFlow-LLaDA-8B-Instruct-Adapt/tulu-3-sft-mixture/checkpoint-final" \
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--tau 0.02 --mask_length 128 --seed 7070 \
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--prompt "write a single-sentence romantic story" --make_gif
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```
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<p align="center">
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<img src="/examples/editflow/assets/deletion.gif" alt="EditFlow deletion demo" width="95%">
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</p>
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<p align="center"><em>Figure: Deletion & Substitution trace</code></em></p>
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<p align="center">
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<img src="/examples/editflow/assets/insertion.gif" alt="LLaDA demo" width="95%">
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</p>
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<p align="center"><em>Figure: Inserction & Substitution trace</em></p>
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## Acknowledgement
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This Edit Flows implementation is inspired by https://github.com/TheMatrixMaster/edit-flows-demo.
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