Open Post-Training System: Building an Open-Source Full-Stack Framework for Large Model Post-Training

Open Post-Training System is an open-source research project focusing on the post-training tech stack for large language models (LLMs), aiming to address the pain point of the lack of a systematic post-training framework in the current open-source community. This framework covers the complete implementation of supervised fine-tuning (SFT), preference optimization, reinforcement learning, inference behavior optimization, evaluation, and scalable inference systems, providing researchers and practitioners with a modular, reproducible post-training technology platform.

With the rapid development of LLMs, the post-training phase (including SFT, preference optimization, RL, etc.) determines the practical value and user experience of models. However, the open-source community lacks a systematic research-level post-training framework covering the entire workflow. Thus, the Open Post-Training System project was born, dedicated to building a modular, reproducible, research-oriented post-training tech stack.

The project adopts a modular design, and its core tech stack includes: 1. Supervised Fine-Tuning (SFT): Implemented based on Hugging Face Transformers and TRL, supporting efficient fine-tuning methods like LoRA/QLoRA; 2. Preference Optimization Algorithms: Integrating mainstream methods such as DPO, ORPO, SimPO; 3. Reinforcement Learning and RLHF: Planning to implement the complete RLHF workflow (reward model training, PPO, etc.); 4. Inference Optimization: Exploring test-time expansion, chain-of-thought reasoning, and self-correction mechanisms.

The dependency ecosystem is based on mature toolchains: Hugging Face Transformers (model loading), TRL (reinforcement learning), vLLM/SGLang (inference services), Ray (distributed training), DeepSpeed/FSDP (parallel training). The design philosophy follows: research priority (clear and modifiable code), reproducibility (complete experimental configurations), minimal abstraction (transparency), and system-level understanding (principle explanations).

1. Academic Research: Provides an experimental platform to support the reproduction of classic methods, verification of new hypotheses, and comparison of technical routes; 2. Industrial Practice: Helps build vertical domain models, implement alignment and safety training, and optimize inference costs; 3. Educational Significance: Clear implementations and documentation help learners build cognition from theory to practice.

Currently in the early active development stage, the core framework has been built. Future plans include: improving the data pipeline, implementing more preference optimization algorithms, building an evaluation system, supporting large-scale distributed training, exploring open-weight inference models, and establishing community collaboration mechanisms.

The project adopts an open collaboration model. Researchers, engineers, and enthusiasts are welcome to participate via GitHub: submit Pull Requests, join discussions, share experiences, and provide feedback to jointly build an active post-training research ecosystem.

Open Post-Training System is a systematic exploration of LLM post-training technology by the open-source community. Against the backdrop of post-training becoming a cost-effective way to enhance model capabilities, this project provides a solid starting point for researchers and practitioners, and is expected to promote the democratization and popularization of post-training technology, allowing more people to participate in AI capability innovation.