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Trial Parallelism accounts for over 65% of reasoning computation in LLMs, and harnessing it can lead to significant speedups in problem-solving.
Self-evolving agents could revolutionize enterprise AI by enabling continuous learning from real-world interactions, but current systems are falling short.
Off-policy distillation fails in multi-task settings, but a two-phase approach combining it with on-policy refinement can achieve single-task expert performance across multiple tasks.
