# Synthesis-Space Expansion

Agentic training-data synthesis has a new shape: EvoSyn turns learner feedback into a revisable synthesis space, so what gets generated follows the task agent's weak spots. Earlier methods used environment feedback, data validation, or learner failures, but observed weaknesses did not directly say what the next round should generate, nor whether existing synthesis dimensions could represent them. On τ-Bench and CAR-Bench with Opus 4.5 as the synthesis model, average task success rises from 64.5% for the strongest synthesis baseline to 77.8%, reaching 70.9% after the first round, with multi-round evolution adding further gains.

Agentic training-data synthesis has a new shape: EvoSyn turns learner feedback into a revisable synthesis space, so what gets generated follows the task agent's weak spots. Earlier methods used environment feedback, data validation, or learner failures, but observed weaknesses did not directly say what the next round should generate, nor whether existing synthesis dimensions could represent them. On τ-Bench and CAR-Bench with Opus 4.5 as the synthesis model, average task success rises from 64.5% for the strongest synthesis baseline to 77.8%, reaching 70.9% after the first round, with multi-round evolution adding further gains.