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Model components / Fine-tuning code

AReaL

AReaL Project

AReaL is a reinforcement learning system for training reasoning and agentic language models. It uses a fully asynchronous training paradigm that decouples rollout generation from policy updates, and supports FSDP and Megatron training backends with vLLM and SGLang inference backends. It was developed by the AReaL Team at Tsinghua IIIS and Ant Group.

Verified 2026-08-31 via the repository LICENSE and README and the PyPI project page.

Openness

5 high confidence
5.0
license
Apache-2.0(OSI)
source
public(areal-project/AReaL)
core-gated
ungated(no enterprise or ee path in the repository root and no paid tier described in the README)

Apache-2.0, an OSI licence, with the full source public and the shipped code in this repository. The LICENSE body was read in full: the stock Apache-2.0 text, its core byte-identical to the canonical template, with nothing after END OF TERMS AND CONDITIONS but the standard appendix. No enterprise or ee path in the repository root and no paid tier described in the readme.

Adoption

1 medium confidence
1.0

What changed here is the instrument, not the level. The PyPI package publishes 1.0.4, uploaded 2026-06-10 and the only release the project has ever made, against repository release v2.1.0 of 2026-08-25 - a whole major line, so anyone on the current version reached it another way and a download count off that line measures a superseded release. A usage_volume record claims to be a download count of the product; this one was a download count of a version the project has moved past, which is the wrong claim rather than a wrong number. The record therefore states no count: reported_traction, no numeric reach, and the 45 downloads kept only as corroboration of the trailing channel. The level is deliberately left where the earlier reading put it. Raising it is a separate judgment needing its own standing evidence, and the only other signal available is the star count, which the routing precedence exists to keep out of an adoption band.

Capability

4 medium confidence
4.0

AReaL's support matrix lists the algorithm, model, training-backend and inference-backend axes that place OpenRLHF at 4: multiple RL algorithms over FSDP/Megatron training with vLLM/SGLang rollout, scaled by TP/PP/EP. It is scored at OpenRLHF rather than above it because the two cover the same ground from different angles - OpenRLHF on Ray-scheduled RLHF at 70B+, AReaL on asynchronous agentic RL - and neither reaches the method catalogue of verl or nemo-rl.

Verified 2026-08-31