Perplexity.AI reduces tool-call failures by 21% with new model training approach

Perplexity.AI has deployed a new self-distillation training method that cut tool-call failures by 21.2% in live testing. For a company whose entire product depends on AI models correctly fetching, parsing, and synthesizing information from external tools, that’s the kind of improvement that separates a useful answer from a hallucinated one. The reduction targets a specific and genuinely annoying problem in modern AI systems. When a language model needs to call an external tool, like a search engine, a calculator, or a code interpreter, it can fail in dozens of ways. It might call the wrong tool, format the request incorrectly, misinterpret the response, or call a tool when it didn’t need to at all. Every one of those failures degrades the final answer a user sees. How self-distillation fixes broken tool calls The approach aligns with a broader framework known as DART-SD that has been gaining traction in the AI research community. DART-SD applies localized self-distillation specifically designed for multi-turn tool-calling agents. The key insight is that it corrects failures without penalizing the valid reasoning steps the model took along the way. Perplexity’s implementation fits i...