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TJUNLP Lab, School of Computer Science and Technology, Tianjin University
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Selective distillation can unlock critical learning signals in reinforcement learning, leading to significant performance gains in complex tasks.
SkillRise achieves up to 8.5 percentage points better performance than leading methods by effectively reusing transferable skills across related tasks.
LLM agents trained with simulated user and tool noise not only become more robust in messy real-world environments, but also surprisingly improve on clean, idealized benchmarks.
SkillSynth's skill graph approach lets you explicitly control the diversity of execution trajectories during terminal task synthesis, leading to more effective agent training.
