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Perplexity AI trains Qwen model for optimized search and tool use

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 1 source

Perplexity AI has developed a new model by post-training on top of Qwen, achieving optimal accuracy-cost trade-offs. This model is specifically engineered for enhanced search capabilities and simultaneous tool usage, integrating the tool call router for unified functionality. This advancement aims to improve the efficiency and effectiveness of AI-driven search and task execution. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Enhances search and tool-calling capabilities, potentially improving AI assistant efficiency.

RANK_REASON A research announcement detailing a new model post-trained on an existing foundation model.

Read on X — Aravind Srinivas (Perplexity) →

COVERAGE [1]

  1. X — Aravind Srinivas (Perplexity) TIER_1 · AravSrinivas ·

    We’ve post trained a model on top of Qwen that achieves Pareto optimality on accuracy-cost curves.

    We’ve post trained a model on top of Qwen that achieves Pareto optimality on accuracy-cost curves. Unlike our previous post trained models, this model has been trained to be good at search and tool calls simultaneously, allowing us to unify the tool call router and

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