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cs.LG2026
Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining
Michael K. Chen, Xikun Zhang, Fan Bai +2
As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, c…
cs.LG2026
FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
Zhengding Hu, Mingge Lu, Zhen Wang +8
LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottleneck. We identify that this…
cs.LG2026
JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training
Zhengding Hu, Hehua Ouyang, Chang Chen +6
We present JigsawRL, a cost-efficient framework that explores Pipeline Multiplexing as a new dimension of RL parallelism. JigsawRL decomposes each pipeline into a Sub-Stage Graph t…