Agent-RL/ReCall

• highabout#1

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

  原因：

  当前

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

  复制粘贴的修复

  复制

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

  待办完成忽略
• highreadme#2

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

  原因：

  当前

  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.

  复制粘贴的修复

  复制

  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.

  待办完成忽略
• mediumtopics#3

  Add more specific topics related to LLM training and agent learning

  原因：

  当前

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

  复制粘贴的修复

  复制

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

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

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

  你：未被推荐
  AI 推荐顺序：

  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 推荐了 8 个替代方案，却始终没点名 Agent-RL/ReCall。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

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

  你：未被推荐
  AI 推荐顺序：

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

  AI 推荐了 7 个替代方案，却始终没点名 Agent-RL/ReCall。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

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

  pass
  ✓AI 明确点名了 Agent-RL/ReCall

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

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

  pass
  ✓AI 明确点名了 Agent-RL/ReCall

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

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

  pass
  ✓AI 明确点名了 Agent-RL/ReCall

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？