WebSailor-V2-30B-A3B Model

A WebSailor-V2-30B-A3B Model is a web navigation AI model that enhances post-training capabilities through reinforcement learning in both simulated and real web environments.

• AKA: WebSailor V2, WebSailor-30B Model, Web Navigation Post-Training Model.
• Context:
  • It can typically navigate WebSailor-V2-30B-A3B Web Tasks through dual environment training combining simulated web environments and real websites.
  • It can typically optimize WebSailor-V2-30B-A3B Performance using group relative policy optimization (GRPO) for token-level rewards.
  • It can typically achieve WebSailor-V2-30B-A3B Benchmark Scores on BrowseComp evaluations and web navigation benchmarks.
  • It can typically enable WebSailor-V2-30B-A3B Multi-Step Navigation through reinforcement learning updates.
  • It can often outperform Baseline Web Agents on navigation accuracy metrics.
  • It can often handle Complex Web Interactions via action sequence planning.
  • It can often support WebSailor-V2-30B-A3B Transfer Learning from simulation to production.
  • It can range from being a Simulation-Only WebSailor-V2-30B-A3B Model to being a Real-World WebSailor-V2-30B-A3B Model, depending on its deployment environment.
  • It can range from being a Basic WebSailor-V2-30B-A3B Model to being a Fine-Tuned WebSailor-V2-30B-A3B Model, depending on its training iteration count.
  • It can range from being a English-Only WebSailor-V2-30B-A3B Model to being a Multilingual WebSailor-V2-30B-A3B Model, depending on its language support.
  • It can range from being a Text-Only WebSailor-V2-30B-A3B Model to being a Multimodal WebSailor-V2-30B-A3B Model, depending on its input modality.
  • ...
• Example(s):
  • WebSailor-V2-30B-A3B Deployments, such as:
    • Tongyi DeepResearch Agent Integration, serving as web navigation backbone.
    • BrowseComp Benchmark Submission, achieving 85% task completion rate.
  • WebSailor-V2-30B-A3B Training Stages, such as:
    • Simulation Training Phase, using synthetic web environments.
    • Real-World Fine-Tuning Phase, on actual websites with 95% success rate.
  • ...
• Counter-Example(s):
  • Static Web Scraper, which lacks dynamic navigation.
  • Rule-Based Web Agent, which uses predetermined paths.
  • Supervised Web Model, which requires labeled navigation data.
• See: Web Agent, Reinforcement Learning Model, Group Relative Policy Optimization (GRPO), BrowseComp Benchmark, Tongyi DeepResearch Agent, Web Navigation Task, Post-Training Model, Qwen3-30B-A3B Model, Agent Navigation System.

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