{"id":482,"date":"2026-02-26T09:09:28","date_gmt":"2026-02-26T09:09:28","guid":{"rendered":"https:\/\/hattussa.com\/blog\/?p=482"},"modified":"2026-02-26T09:10:56","modified_gmt":"2026-02-26T09:10:56","slug":"training-multi-agentic-systems-for-complex-task-planning-with-grpo-algorithm","status":"publish","type":"post","link":"https:\/\/hattussa.com\/blog\/training-multi-agentic-systems-for-complex-task-planning-with-grpo-algorithm\/","title":{"rendered":"Training Multi-Agentic Systems for Complex Task Planning with GRPO Algorithm"},"content":{"rendered":"
\n
\n

<\/p>\n

\n
Table of contents<\/div>\n
    \n
  • Introduction to Multi-Agent GRPO<\/li>\n
  • Data Ingestion & Preparation<\/li>\n
  • Agentic Inference Engine<\/li>\n
  • GRPO Training Loop<\/li>\n
  • The Future of Agentic AI Systems<\/li>\n<\/ul><\/div>\n

    <\/p>\n

    \n

    <\/p>\n

    \n

    \ud83d\ude80 Training Multi-Agentic Systems for Complex Task Planning with GRPO Algorithm<\/h2>\n

    \n This advanced AI pipeline demonstrates how
    \n multi-agent systems<\/strong> can be trained to solve complex reasoning
    \n and planning tasks using the GRPO (Group Relative Policy Optimization)<\/strong> algorithm.\n <\/p>\n

    \n It represents a shift from single-model workflows to coordinated, intelligent agent ecosystems.\n <\/p>\n<\/section>\n

    <\/p>\n

    \n

    \ud83d\udd0d Phase 1: Data Ingestion & Preparation<\/h2>\n

    \n The process begins with large-scale datasets such as
    \n DeepMath<\/strong> and Natural Questions<\/strong>.\n <\/p>\n

    \n Data undergoes:\n <\/p>\n

      \n
    • \ud83d\udcca Normalization<\/li>\n
    • \ud83e\udde9 Schema mapping<\/li>\n
    • \ud83d\udcbe Structured storage in Parquet format<\/li>\n<\/ul>\n

      \n This ensures clean, unified, and high-quality data ready for training.\n <\/p>\n<\/section>\n

      <\/p>\n

      \n

      \ud83e\udde0 Phase 2: Agentic Inference Engine<\/h2>\n

      \n A powerful planner model (Qwen with LoRA adapters)<\/strong> works together
      \n with Executor and Verifier agents.\n <\/p>\n

      \n The system integrates tools such as:\n <\/p>\n

        \n
      • \ud83d\udc0d Python code execution<\/li>\n
      • \ud83d\udcda Wikipedia RAG search<\/li>\n
      • \ud83c\udf10 Google search integration<\/li>\n
      • \ud83d\udcdd Memory logging and storage<\/li>\n<\/ul>\n

        \n This enables dynamic reasoning, execution, verification, and learning in real-time.\n <\/p>\n<\/section>\n

        <\/p>\n

        \n

        \u2696\ufe0f Phase 3: GRPO Training Loop<\/h2>\n

        \n Using a Judge model (GPT-4o)<\/strong>, multiple rollout trajectories are evaluated.\n <\/p>\n

        \n The training process includes:\n <\/p>\n

          \n
        • \ud83d\udcc8 Reward calculation<\/li>\n
        • \ud83d\udcca Advantage normalization<\/li>\n
        • \ud83d\udd01 PPO updates with KL penalty constraints<\/li>\n<\/ul>\n

          \n This ensures stable, optimized, and efficient learning.\n <\/p>\n<\/section>\n

          <\/p>\n

          \n

          \u2728 The Future of Agentic AI Systems<\/h2>\n

          \n This architecture represents the evolution from
          \n single-model prompting<\/strong> to
          \n coordinated, tool-augmented, memory-driven AI agents<\/strong>.\n <\/p>\n

          \n These systems are capable of:\n <\/p>\n

            \n
          • \u2705 Structured reasoning<\/li>\n
          • \u2705 Complex task planning<\/li>\n
          • \u2705 Adaptive decision-making<\/li>\n
          • \u2705 Autonomous execution<\/li>\n<\/ul>\n

            \n
            \n The future of AI is not just smarter models \u2014 it\u2019s smarter systems.
            \n <\/strong>\n <\/p>\n<\/section><\/div>\n<\/p><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

            This advanced AI pipeline demonstrates how multi-agent systems<\/strong> can be trained to solve complex reasoning and planning tasks using the GRPO (Group Relative Policy Optimization)<\/strong> algorithm.<\/p>\n","protected":false},"author":1,"featured_media":479,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-482","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/posts\/482","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/comments?post=482"}],"version-history":[{"count":1,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/posts\/482\/revisions"}],"predecessor-version":[{"id":483,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/posts\/482\/revisions\/483"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/media\/479"}],"wp:attachment":[{"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/media?parent=482"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/categories?post=482"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hattussa.com\/blog\/wp-json\/wp\/v2\/tags?post=482"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}