XYZ-AI-Lab/axrl

Health v2 · maintenance only

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984 stars · 24 forks observed · 2026-10-04

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AxisRL is an agentic reinforcement learning post-training framework for large language models, built on SGLang for high-throughput rollout and Megatron for large-scale distributed training. It coordinates multi-turn agent environments, tool calls, reward collection, weight synchronization, and reproducible debugging within a single system layer.

Use cases

• post-train an LLM with PPO or GRPO on multi-turn agent trajectories
• run RL training with hundreds-of-turn tool-calling agent workflows
• scale rollout and training across a GPU cluster with SGLang and Megatron
• capture black-box agent environments through an OpenAI-compatible proxy for RL training
• debug rollout-training mismatches with spike replay and routing analysis

When to choose

• you need agentic multi-turn RL post-training at large parameter scale
• you want SGLang rollout combined with Megatron distributed training in one framework
• you need configurable policy objectives like PPO, GRPO, GSPO, or TOPR
• you require observability and reproducibility for long agent training runs

When to avoid

• you only need single-turn supervised fine-tuning without RL
• you lack multi-GPU infrastructure or a serving cluster
• you need a simple plug-and-play RLHF library with minimal setup
• you work outside the Python 3.12+ ecosystem

Facets

framework · maturity active

llm-training agent-framework llm-inference machine-learning gpu-computing large-language-models machine-learning reinforcement-learning gpu-computing python reinforcement-learning post-training sglang megatron rlhf agentic-rl ppo grpo rollout distributed-training ai-agents gpu linux docker

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markdown · JSON · MCP: product_card(name="XYZ-AI-Lab/axrl")