# Agentic Data Synthesis

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.

SynCo formulates agentic data synthesis and task solving as a coupled multi-agent reinforcement learning problem, updating a Synthesizer and a Reasoner from the same online interaction batch: the Reasoner receives rollout-level correctness rewards while the Synthesizer receives a task-level reward gated by task quality, answer reliability, and outcome-grounded teachability, with both updates feeding the next synthesis round. Across eight mathematical reasoning benchmarks, SynCo reaches 78.72% average accuracy versus 75.85% for a frozen-Synthesizer variant and 74.96% for the strongest external baseline, ScaleQuest-Math, with gains arising mainly from problems the initial model failed to solve.

SynCo formulates agentic data synthesis and task solving as a coupled multi-agent reinforcement learning problem, updating a Synthesizer and a Reasoner from the same online interaction batch: the Reasoner receives rollout-level correctness rewards while the Synthesizer receives a task-level reward gated by task quality, answer reliability, and outcome-grounded teachability, with both updates feeding the next synthesis round. Across eight mathematical reasoning benchmarks, SynCo reaches 78.72% average accuracy versus 75.85% for a frozen-Synthesizer variant and 74.96% for the strongest external baseline, ScaleQuest-Math, with gains arising mainly from problems the initial model failed to solve.