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This study advances agent reliability by leveraging real-world failure data through a novel combination of rejection sampling and hint-guided self-distillation, directly addressing tool-call errors without discarding valuable negative examples. The approach demonstrates that correcting specific erroneous turns while preserving successful context can yield statistically significant improvements in live deployment scenarios. While the post-trained weights remain proprietary to Perplexity Computer, the methodology offers a concrete pathway for improving autonomous agent robustness using accessible base models like GLM 5.2.

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Perplexity Trains Its Computer Agent on Real Mistakes With Hint-Guided Self-Distillation

MarkTechPost25 septembrie 20264 min de cititArticolul și perspectiva sunt în engleză.

Perplexity Research published a new post-training study. It trains a model inside Perplexity Computer on real user sessions, including failed ones. The method pairs rejection sampling fine-tuning with hint-guided self-distillation. In a live A/B test, tool-call failures fell from 2.24% to 1.77% between 2 trained checkpoints. Perplexity team reports this as a statistically significant 21.2% […] The post Perplexity Trains Its Computer Agent on Real Mistakes With Hint-Guided Self-Distillation appeared first on MarkTechPost.

Perplexity Research published a new post-training study. It trains a model inside Perplexity Computer on real user sessions, including failed ones. The method pairs rejection sampling fine-tuning with hint-guided self-distillation. In a live A/B test, tool-call failures fell from 2.24% to 1.77% between two trained checkpoints. The Perplexity team reports this as a statistically significant 21.2% relative reduction. Is it deployable? Not directly. Perplexity has not released the post-trained weights or training code. The model runs only as a…

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