Ml-Diffucoder

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ml-diffucoder is an open-source research project that provides masked diffusion models for code generation. It offers insights into improving code generation using diffusion models and includes a post-training method called Coupled-GRPO.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is ml-diffucoder?

ml-diffucoder is an open-source research project accompanying the paper "DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation." It explores how diffusion LLMs (dLLMs) differ from autoregressive models in code generation, investigates data modality differences (code vs. math), and examines diversity and post-training strategies for dLLMs. The project introduces Coupled-GRPO, a novel post-training method designed to enhance DiffuCoder's performance by improving the efficiency and accuracy of log-probability computations during training. The repository provides the implementation of Coupled-GRPO, built upon open-r1, along with scripts for training, data preparation, and inference. It also offers pre-trained DiffuCoder models (Base, Instruct, and cpGRPO) on HuggingFace, complete with usage examples for both base code completion and chat-based instruction following.

Best used for

Ideal for researchers and developers who need to explore the capabilities of masked diffusion models for code generation, understand their generation patterns, and improve their performance. Especially valuable for those interested in novel post-training methods like Coupled-GRPO and integrating pre-trained models into their projects.

Common actions

generate code

research code generation

improve diffusion models

evaluate code models

open-sourceautomated workflowcollaborationdeepfakelow-code/no-codeface swapping"AI Agents"workflowsgithub copilot

Capabilities

Key features

Masked diffusion models
Coupled-GRPO post-training
Code generation
Pre-trained models
Inference examples
Code evaluation

Target Audience

developerprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is Coupled-GRPO and how does it improve DiffuCoder?

Coupled-GRPO is a post-training method introduced in ml-diffucoder that improves the model's performance. It uses a coupled-sampling scheme during training to ensure every token's log-probability is computed at least once, providing a non-zero learning signal and more accurate probability estimates in a realistic partially-masked context, with modest computational overhead.

Where can I find the pre-trained DiffuCoder models?

The DiffuCoder models (Base, Instruct, and cpGRPO) are available on HuggingFace. The repository provides examples and instructions on how to load and use these models for inference, including both base code completion and chat-based instruction following scenarios.

What are the key findings from the DiffuCoder research?

The research found that dLLMs exhibit a left-to-right bias but can break strict AR order, code tasks induce less global AR-ness than math, and changing sampling temperature in dLLMs alters both sampled tokens and generation order. These findings offer deeper insights into diffusion model behavior for code generation.

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