Alibaba-NLP/ZeroSearch

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1307 stars · 119 forks observed · 2026-08-28

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

ZeroSearch is a reinforcement learning framework from Alibaba's Tongyi Lab that trains LLMs to use search by simulating search engine results with a fine-tuned LLM instead of calling real search APIs. It includes simulation LLMs, policy models, tuning datasets, and support for REINFORCE, GRPO, and PPO algorithms.

Use cases

• train an LLM to use search without paying for search API calls
• reinforcement learning fine-tuning of LLMs for search agents
• simulate search engine results for LLM training
• train a search-capable agent with PPO or GRPO
• research on incentivizing search capabilities in language models

When to choose

• you want to RL-train LLMs for search/retrieval without real search API costs
• you need reproducible simulated search environments for agent training
• you want pretrained simulation and policy models for Google or Wikipedia search

When to avoid

• you need a production search engine or RAG pipeline rather than training
• you lack GPU resources for LLM training
• you need a plug-and-play search tool for inference only

Facets

library · maturity active

llm-training rag machine-learning agent-framework large-language-models machine-learning python reinforcement-learning search-simulation llm-finetuning research verl retrieval-augmented-generation ai-agents gpu linux

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markdown · JSON · MCP: product_card(name="Alibaba-NLP/ZeroSearch")