Adding corrective hints allowed an unchanged model to avoid failures in 93.7% of cases, up from 75.1% without hints, while reducing overall failure rates by about 21% in live A/B tests. The training made agent trajectories more cost-efficient and showed gains in user satisfaction that are not yet statistically significant. Perplexity noted this approach provides a potential path for online and continual learning for AI agents.
Tuesday, October 20, 2026
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Perplexity Cuts Agent Tool Failures 21% Using New GLM-5.2 Training
AI28 days ago
The error rate for tool calls in Perplexity's Computer agent fell from 2.24% to 1.77% during separate live tests after the company applied new post-training techniques to its GLM-5.2 model. The method uses rejection sampling fine-tuning and hint-guided self-distillation, also known as on-policy self-distillation, to teach the agent to learn from real user sessions by imitating successful trajectories and correcting mistakes such as bad tool calls.
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