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Contrastive flow matching can upper-bound forward-KL divergence on fixed preference pairs, resolving the training instability of unbounded DPO surrogates without requiring online rollouts.
Frontier-scale agentic RL on 700B+ MoEs is no longer locked inside hyperscaler proprietary stacks, achieving stable 263-second step times across 64 GB300 GPUs via a verified, open-source training infrastructure.
High-fidelity image synthesis does not require paired text from day one: pre-training visual priors on uncaptioned images before multimodal alignment beats conventional joint training pipelines to establish a new open-source DiT benchmark.
