v0.2.0 release

[PanAndy]

Hello everyone! Thank you for your attention to ROLL.
ROLL has recently updated with a large number of new features. Below is a summary of recent updates, and we will continue to iterate and update ROLL. Welcome to join the ROLL community.

🚀 Highlights:

• New model support: Qwen3-VL, Qwen3-MoE-VL, Qwen3-Omni, GLM-4.7
• Agentic training and Rollout GPU partial overlap, switching idle training GPUs to Rollout
• DynamicSamplingScheduler coroutine refactoring
• New: FSDP2 Strategy
• Training supports Sequence packing and Dynamic batching

🚀 Major New Features:

• Rollout
  • DynamicSamplingScheduler coroutine refactoring
  • Custom rollout pre/post process, supporting dynamic sampling params, multi-stage generation, ThinkingBudget control
  • Sglang: Strategy refactoring, supporting server mode, native onload/offload, inflight FP8 quant rollout, cross-machine multi-node deployment
  • vLLM: DP/EP support, supports vllm==0.12.0
  • Provides AgentNative Rollout paradigm, AgentNativeStepEnvManager + SokobanNativeEnv, fully managed context by env
  • Async Rollout Hang Detect: Added asynchronous Rollout hang detection to quickly locate problematic envs
  • Supports rollout dump & mock, improving forward/train phase precision alignment efficiency
  • Agentic pipeline supports train-val/rollout overlap
• Training
  • FSDP2
  • Megatron support LoRA, LoRA RL blogs: https://macaron.im/mindlab/research/building-trillion-parameter-reasoning-rl-with-10-gpus
  • Save model parameters in HF format online during Megatron training
  • Support FP8 training for Megatron Strategy
  • Sequence packing, fine-tuned loss_func interface definition
  • Dynamic batching
  • Add DeepSpeed SFT support
• Model Update implementation optimization: Eliminate inter-machine redundancy, weight conversion and nccl broadcast overlap, optimize host to device, adjust multiple pp serial synchronization to lock mode for simultaneous synchronization
• Asynchronous Feature
  • Training and Rollout GPU partial overlap, switching idle training GPUs to Rollout, report: https://arxiv.org/abs/2512.24873
  • Agentic off policy loss with IS correction
• Pipeline recipe
  • VLM image tool use: DeepEyes, tool invocation and reward calculation overlap
• Models: New model support for Qwen3-VL, Qwen3-MoE-VL, Qwen3-Omni-Thinker, GLM-4.7

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This discussion was created from the release v0.2.0 release.