Agent-RL/ReCall

• highabout#1

  Condense and focus the repository description on ReCall's core value

  Why:

  CURRENT

  ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning & ReCall: Learning to Reason with Tool Call for LLMs via Reinforcement Learning

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  ReCall: A framework for training LLMs to reason with tool calls via reinforcement learning, without requiring supervised data on tool use trajectories.

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• highreadme#2

  Refine the README's opening paragraph for immediate impact on unique value

  Why:

  CURRENT

  We introduce ReCall, a novel framework that trains LLMs to Reason with Tool Callvia reinforcement learning—without requiring any supervised data on tool use trajectories or reasoning steps. *ReCall* empowers LLMs to agentically use and combine arbitrary tools like OpenAI o3, offering an accessible approach toward general-purpose agents. Additionally, we provide a novel perspective to generate synthetic data with diverse environments and complex multi-step tasks, enabling LLMs to develop sophisticated tool-based reasoning capabilities. This is a work in progress and we are actively working on it.

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  ReCall is a novel framework for training Large Language Models (LLMs) to reason with tool calls via reinforcement learning, *without requiring any supervised data on tool use trajectories or reasoning steps*. It empowers LLMs to agentically use and combine arbitrary tools, offering an accessible approach toward general-purpose agents capable of sophisticated multi-step reasoning.

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• mediumtopics#3

  Add more specific topics related to LLM training and agent learning

  Why:

  CURRENT

  agent, function-calling, llm, reinforcement-learning, tool-use

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  agent, function-calling, llm, reinforcement-learning, tool-use, llm-training, agent-learning

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How to train large language models for complex tool use without extensive supervised examples?

  you: not recommended
  AI recommended (in order):

  1. Google's Self-Refine
  2. Anthropic's Constitutional AI
  3. OpenAI Code Interpreter
  4. Google's AlphaCode
  5. LangChain
  6. LlamaIndex
  7. GPT-4
  8. Claude 3 Opus

  AI recommended 8 alternatives but never named Agent-RL/ReCall. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What framework helps LLMs agentically combine user-defined tools for sophisticated multi-step reasoning?

  you: not recommended
  AI recommended (in order):

  1. LangChain
  2. LlamaIndex
  3. Haystack
  4. AutoGPT
  5. CrewAI
  6. Microsoft Guidance
  7. OpenAI Assistants API

  AI recommended 7 alternatives but never named Agent-RL/ReCall. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of Agent-RL/ReCall?

  pass
  ✓AI named Agent-RL/ReCall explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts Agent-RL/ReCall in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named Agent-RL/ReCall explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo Agent-RL/ReCall solve, and who is the primary audience?

  pass
  ✓AI named Agent-RL/ReCall explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?