AReaL Roadmap

This roadmap outlines the planned features and improvements for AReaL in the next quarter. We welcome community feedback and contributions to help shape the future direction of the project.

Latest Release: Check releases for the most recent version.

2026 H2 Roadmap (due December 31, 2026)

GitHub Issue #1381.

This roadmap tracks major planned enhancements for the second half of 2026. Items are organized into two categories:

• On-going: Features currently under active development by the core AReaL team
• Planned but not in progress: Features that are good to have but currently lacking bandwidth

We use [CC] to mark items suitable for community contributions. If you're interested in contributing, please reach out to discuss implementation details.

Backends

On-going

• Support training the latest large MoE models with the Megatron backend (including Kimi 2.5 / GLM 5 / DeepSeek V3 / Qwen 3.6, etc.) (#1372, #1373)
• More distribution strategies for colocated deployment

Planned but not in progress

• Omini model RL support with FSDP backend
• Proxy server support via RayScheduler (HTTP launcher actor) [CC]
• Weight delta update with awex
• Memory service for self-evolving agent

Usability

On-going

• Effective AReaL CLI support (#1374)
• Profiling toolkit (distributed trace)
• Online RL training example based on AReaL 2.0 architecture

Planned but not in progress

• More RL post-training paradigms: IcePop Plus algorithm (verified in Ling MoE training) and stepwise reward rubrics
• GUI Agent VLM training example
• OS-bench VLM training example [CC]
• Diffusion image/video generation model RL post-training [CC]
• Comprehensive LoRA support over the existing policy model training
• AReaL autopilot (automatic performance-optimized RL deployment suggestions and systematic bottleneck analysis)

Documentation

Planned but not in progress

• Explain benchmarking results from nightly CI and how to extract issues

2026 Q2 Roadmap (due July 31, 2026)

GitHub Issue #1302.

This roadmap tracks major planned enhancements through July 31, 2026. Items are organized into two categories:

• On-going: Features currently under active development by the core AReaL team
• Planned but not in progress: Features that are good to have but currently lacking bandwidth

We use [CC] to mark items suitable for community contributions. If you're interested in contributing, please reach out to discuss implementation details.

Backends

On-going

• Full training example with AReaL 2.0 architecture
• Deprecate support for SPMD mode (launcher, sglang/vllm server, etc.)
• Initial support of colocation weight transfer with awex

Planned but not in progress

• Migrate primary Megatron integration lib from mbridge to megatron-bridge (#1260) [CC]
• Full support for colocation/separation weight transfer with awex as the backend (areal/experimental/weight_update/) [CC]
• Migrate legacy NCCL broadcast weight transfer approach from areal/engine into areal/experimental/weight_update/ [CC]
• Omini model RL support with FSDP backend (#879) [CC]
• Support training the latest large MoE models with the Megatron backend, including dpsk-v3/v4, Kimi-2.5, GLM-4/5 [CC]
• Native Kubernetes (K8S) scheduler [CC]

Usability

On-going

• Nightly CI workflow for performance benchmarking (#1284)
• Refactor unit tests for faster execution

Planned but not in progress

• OS-bench VLM training example [CC]
• Multi-agent training example (single LLM, different prompts, e.g., planner agent with sub-agents) [CC]
• Migrate legacy multi-turn agent examples to new API (agenerate → ArealOpenAI or URL-based AgentWorkflow) [CC]
• Publish PyPI packages and CLI for running experiments
• Support distributed training and debugging in Jupyter notebooks
• Implement controller construction with model-centric API similar to transformers

Documentation

Planned but not in progress

• Explain benchmarking results from nightly CI and how to extract issues
• Document AReaL 2.0 architecture

2026 Q1 Roadmap (due April 30, 2026)

GitHub Issue #907.

This roadmap tracks major planned enhancements through April 30, 2026. Items are organized into two categories:

• On-going: Features currently under active development by the core AReaL team
• Planned but not in progress: Features with concrete implementation plans where we welcome community contributions

Backends

On-going

• ZBPP & ZBPP-V support for the Archon backend
• FP8 training for Archon

Planned but not in progress

• Support for agentic training with large VLM MoE models (Archon backend)
• Omini model RL support with FSDP/Archon backend
• Decoupling agent service from the inference service
• Online RL training with the proxy server
• LoRA support for the Archon backend
• Colocation mode with awex as the weight sync engine
• Multi-LLM training (different agents with different parameters)
• Auto-scaling inference engines in single-controller mode
• Elastic weight update setup and acceleration
• RL training with cross-node vLLM pipeline/context parallelism

Usability

On-going

• Flatten the import structure of areal modules

Planned but not in progress

• Publishing PyPI packages
• Support distributed training and debugging in Jupyter notebooks
• Example of using a generative or critic-like reward model
• Support directly constructing inference/training engines without config objects
• Add router in rollout controller for simpler proxy server usage
• Integrate aenvironment for environment handling

Documentation

Planned but not in progress

• Use case guides: multi-agent training
• Guide for online proxy mode training

Historical Roadmaps

2025 Q4

GitHub Issue #542.

Backends

Completed:

• Single-controller mode
• Detailed profiling for optimal performance across different scales
• Low-precision RL training (Megatron FP8)
• Data transfer optimization in single-controller mode
• New PyTorch-native backend: Archon

Carried over to Q1 2026:

• Multi-LLM training (different agents with different parameters)
• Auto-scaling inference engines in single-controller mode
• Elastic weight update setup and acceleration
• RL training with cross-node vLLM pipeline/context parallelism

Usability

Completed:

• Add CI pipeline to build Docker images upon release
• Wrap training scripts into trainers
• Refactor FSDP/Megatron engine/controller APIs to finer granularity
• Fully respect allocation mode in trainers/training scripts

Carried over to Q1 2026:

• Flatten the import structure of areal modules
• Support distributed training and debugging in Jupyter notebooks
• Example of using a generative or critic-like reward model

Canceled:

• Rename RemoteSGLang/vLLMEngine as SGLang/vLLMEngine

Documentation

Completed:

• Tutorial on how to write efficient async rollout workflows
• Benchmarking and profiling guide
• Use case guides: offline inference, offline evaluation
• AReaL performance tuning guide
  • Device allocation strategies for training and inference
  • Parallelism strategy configuration for training and inference

Carried over to Q1 2026:

• Use case guides: multi-agent training

2025 Q3

GitHub Issue #257.

Backends

Completed:

• Megatron training backend support
• SGLang large expert parallelism (EP) inference support
• Remote vLLM inference engine
• Ulysses context parallelism & tensor parallelism for FSDP backend
• End-to-end MoE RL training with large EP inference and Megatron expert parallelism
• Distributed weight resharder for Megatron training backend

Canceled:

• Local SGLang inference engine with inference/training colocation (hybrid engine)
• RL training with SGLang pipeline parallelism

Usability

Completed:

• OpenAI-compatible client support
• Support RLOO
• Provide benchmarking configuration examples:
  • DAPO
  • Bradley-Terry reward modeling
  • PPO with critic models
  • REINFORCE++

Documentation

Completed:

• OpenAI-compatible client documentation
• Out-of-memory (OOM) troubleshooting guide
• AReaL debugging best practices:
  • LLM server-only debugging - How to launch LLM servers independently and debug agent workflows
  • Mock data and torchrun debugging - Creating synthetic data and using torchrun for algorithm debugging
  • Training-free evaluation experiments - Running evaluations without training or additional GPUs

How to Influence the Roadmap

We value community input! Here's how you can help shape AReaL's future:

💡 Propose New Features

1. Check Existing Issues: Search issues and discussions to see if your idea already exists
2. Create a Feature Request: Use our feature request template
3. Discuss in GitHub Discussions: Post in Ideas category for early feedback
4. Vote on Features: Use 👍 reactions on issues to show support

🛠️ Contribute Implementation

Check our contribution guide.

Release Cycle

Minor Releases: Bi-weekly - Bug fixes, small improvements, and new features

Major Releases: Quarterly - Important milestones and significant changes

Historical Milestones

Check our historical milestone summaries since open-source.

Long-Term Vision

Our vision for AReaL is to become the go-to framework for training reasoning and agentic AI systems that is:

1. Accessible: Easy to get started, whether you're a researcher or practitioner
2. Scalable: Scales from laptop to 1000+ GPU clusters seamlessly
3. Flexible: Supports diverse algorithms, models, and use cases
4. Performant: Industry-leading training speed and efficiency
5. Open: Fully open-source with transparent development

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Last Updated: 2026-06-05

Questions about the roadmap? Open a discussion in GitHub Discussions or ask in our WeChat group.