AgentFounder-30B Model

An AgentFounder-30B Model is an AI pretraining model that instills agent-based behaviors through continual pre-training on synthetic question-answer pairs derived from knowledge graphs.

• AKA: AgentFounder-30B, Agent Founder Model, 30B Agent Pretraining Model.
• Context:
  • It can typically synthesize AgentFounder-30B Training Data through first-order action synthesis and knowledge graph extraction.
  • It can typically refine AgentFounder-30B Agent Trajectorys via high-order action synthesis and trajectory optimization.
  • It can typically enable AgentFounder-30B Reasoning Capabilitys through set-theory reasoning and logical inference training.
  • It can typically support AgentFounder-30B Pipeline Integration with downstream agent models.
  • It can often improve Agent Task Performance through behavior pretraining.
  • It can often generate Synthetic Q&A Datasets from structured knowledge sources.
  • It can often complement Supervised Fine-Tuning Methods in agent training pipelines.
  • It can range from being a Basic AgentFounder-30B Model to being an Advanced AgentFounder-30B Model, depending on its synthetic data complexity.
  • It can range from being a Small-Scale AgentFounder-30B Model to being a Large-Scale AgentFounder-30B Model, depending on its training corpus size.
  • It can range from being a Single-Domain AgentFounder-30B Model to being a Multi-Domain AgentFounder-30B Model, depending on its knowledge graph coverage.
  • It can range from being a Frozen AgentFounder-30B Model to being a Continually-Updated AgentFounder-30B Model, depending on its training schedule.
  • ...
• Example(s):
  • AgentFounder-30B Applications, such as:
    • Tongyi DeepResearch Agent Pretraining, which uses it for agentic behavior initialization.
    • WebSailor Model Training, which leverages it for navigation capability.
  • AgentFounder-30B Training Methods, such as:
    • First-Order Action Synthesis, generating basic action sequences.
    • High-Order Action Synthesis, creating complex reasoning chains.
  • ...
• Counter-Example(s):
  • Human-Labeled Pretraining Model, which uses manual annotations.
  • Static Dataset Training, which lacks synthetic generation.
  • Standard Supervised Pretraining, which uses existing corpus.
• See: Agentic Continual Pre-training (CPT), Synthetic Data Generation, Knowledge Graph, Pretraining Model, Tongyi DeepResearch Agent, Qwen3-30B-A3B Model, Agent Training Pipeline, Set Theory Reasoning, AI Model Component.

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