Training AI From Real-World Tool Use

Back to Devoured - September 23, 2026

DEVOURED

Training AI From Real-World Tool Use

Perplexity improved its Computer model by training on real-world tool use feedback, incorporating both successful paths and user-corrected failures.

What: Perplexity refined its model by combining rejection sampling—filtering for the best outcomes—with hint-guided training to learn from successful and failed user interactions.

Why it matters: This demonstrates a shift toward training agents based on multi-turn user-in-the-loop corrections rather than just static text corpora.

Decoder

Rejection sampling: A technique where multiple outputs are generated, and only those that meet specific quality criteria are kept for training.

Original article

Perplexity combined rejection sampling fine-tuning with hint-guided self-distillation so its Computer model could learn from both successful sessions and user-corrected failures.

Explore all 56 articles in today's digest