🍭[Roadmap] ms-swift 4.2-4.4

中文版

重构

1. dataset模块：✅样本级别控制 enable_thinking, max_pixels等；streaming训练速度优化。
2. 模型模块：transformers 分类/embedding/reranker任务lm_head部分逻辑统一，方便维护。
3. infer_engine模块，数据预处理异步处理；异常处理优化。
4. 序列并行 (ulysses/ring attention) 模块重构。
5. Web-UI模块重构。

Megatron-SWIFT

1. 新模型：✅deepseek-v4, ✅gemma4, mimo-v2.5, OLMoE2, ✅ring2.5, ✅qwen3_asr
2. 多模态部分的并行支持
3. ✅GLM-5 共享MTP支持
4. ✅Qwen3.5 FP8训练
5. ✅自定义Megatron模型文档
6. FP4支持
7. megatron-fsdp 支持
8. ✅镜像使用cuda13，flash-attn-4

RL

1. ✅RAY & Megatron (GRPO/GKD)
2. ✅Megatron GRPO/GKD 权重同步 支持仅同步LoRA
3. ✅Megatron 多轮RL
4. ✅GKD/OPSD teacher_server_api 兼容多模态训练
5. ✅GKD/OPSD buffer与采样速度优化
6. vLLM原生权重同步接口支持
7. ✅新算法支持：FIPO
8. ✅distillation RL (KL advantages)
9. fully async rollout

训练

1. ✅新模型 day0支持
2. ✅Qwen3-TTS支持训练；Omni talker的训练
3. ✅Qwen3-Embedding MRL 支持
4. ✅ulysses/ring attention支持Qwen3.5
5. 训练中 验证/评测 支持vllm推理

全链路

1. sglang 多模态模型
2. vllm-omni

English Version

Refactoring

1. Dataset module: ✅Sample-level control for enable_thinking, max_pixels, etc.; streaming training speed optimization.
2. Model module: Unified lm_head logic for transformers classification/embedding/reranker tasks, improving maintainability.
3. infer_engine module: Asynchronous data preprocessing; optimized exception handling.
4. Sequence Parallelism (Ulysses / Ring Attention) module refactored.
5. Web-UI module refactored.

Megatron-SWIFT

1. New models: ✅deepseek-v4, ✅gemma4, mimo-v2.5, OLMoE2, ✅ring2.5, ✅qwen3_asr
2. Parallel support for multimodal components.
3. ✅GLM-5 shared MTP support.
4. ✅Qwen3.5 FP8 training.
5. ✅Custom Megatron model documentation.
6. FP4 support.
7. megatron-fsdp support.
8. ✅Docker image upgraded to CUDA 13 with flash-attn-4.

RL

1. ✅RAY & Megatron (GRPO/GKD).
2. ✅Megatron GRPO/GKD weight synchronization now supports syncing LoRA weights only.
3. ✅Megatron multi-turn RL.
4. ✅GKD/OPSD teacher_server_api is now compatible with multimodal training.
5. ✅GKD/OPSD buffer and sampling speed optimization.
6. vLLM native weight synchronization interface support.
7. ✅New algorithm support: FIPO.
8. ✅distillation RL (KL advantages)
9. fully async rollout

Training

1. ✅Day-0 support for new models.
2. ✅Qwen3-TTS training support; Omni Talker training support.
3. ✅Qwen3-Embedding MRL support.
4. ✅Ulysses/Ring Attention support for Qwen3.5.
5. Validation/evaluation during training with vLLM inference support.

Full Pipeline

1. SGLang multimodal model support.
2. vllm-omni support.

This is a broad roadmap covering the dataset, model, infer_engine, sequence-parallelism, Web-UI, Megatron-SWIFT, RL, training, and full-pipeline areas. Start by selecting one unchecked item and reading its relevant module and existing support; done should mean that one scoped capability is implemented and validated.