Perplexity's post-training method cuts agent tool call failures by 21%
beffjezos · x · 2026-09-23
- Perplexity shared new research on post-training its Computer agent: learning from real user sessions by imitating good trajectories and explicitly correcting avoidable mistakes like bad tool calls, even when the overall trajectory succeeded.
- The method combines rejection sampling fine-tuning (RFT) with hint-guided self-distillation.
- Live A/B tests show tool call failures cut by 21%.
- beffjezos amplified it, arguing there's "huge alpha" in post-training custom models that too few people are talking about.
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