RUC-NLPIR/ARPO

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1109 stars · 60 forks observed · 2026-08-28

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

ARPO (Agentic Reinforced Policy Optimization) is a reinforcement learning algorithm and training framework for LLM agents, published at ICLR 2026. It provides training code, models, and datasets for improving multi-turn agentic reasoning and tool use via RL.

Use cases

• train llm agents with reinforcement learning
• improve multi-turn tool use of a language model
• reproduce ARPO paper results
• fine-tune Qwen or Llama models for agentic search
• apply RL post-training to deep search agents

When to choose

• you need a research-grade agentic RL training algorithm for LLMs
• you want to RL-train models for multi-turn tool calling or deep search
• you want to build on ICLR 2026 ARPO/AEPO methods

When to avoid

• you need a production-supported training framework with commercial support
• you need simple SFT-only fine-tuning without RL
• you lack GPU infrastructure for large-model RL training

Facets

library · maturity active

llm-training reinforcement-learning agent-framework large-language-models reinforcement-learning machine-learning python rlhf policy-optimization agentic-rl iclr-2026 research-code llm-post-training ai-agents gpu linux

Member repositories

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markdown · JSON · MCP: product_card(name="RUC-NLPIR/ARPO")