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Scaling recursive self-improvement requires additional computation to produce useful research progress in reaching persistent objectives. Intuitively, more researcher agents can explore more hypotheses, but early evaluation scores might misrepresent their promise, shared findings could lose their applicability conditio...
Reinforcement learning (RL) post-training often uses distinct GPU kernels for rollout and policy update. In synchronous PPO and GRPO, numerical disagreement can perturb ratios between current token probabilities and those assigned during rollout. Recomputing rollout log-probabilities with the policy-update backend avoi...
AReaL-DTE is presented, a snapshot-free Delta Transfer Engine that translates inference-visible weight sparsity into end-to-end system efficiency and achieves speedups of up to 19.9x over ByteCheckpoint and 3.2x over PULSE across clusters, and up to 7.6x and 7.4x within a cluster.
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