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Decoupling task synthesis from on-policy rollouts solves the learning-signal saturation bottleneck in terminal agents, boosting long-horizon RL performance by up to 18 percentage points on Terminal-Bench 2.1.
SARA unlocks the potential of low-resource languages in multilingual models by aligning their expert routing with high-resource anchors, leading to measurable performance gains.
SkillSynth's skill graph approach lets you explicitly control the diversity of execution trajectories during terminal task synthesis, leading to more effective agent training.
Forget rewriting single web pages – WRAP++ unlocks a 10x data scale-up and boosts LLM knowledge by synthesizing QA pairs that demand reasoning across multiple linked documents.
