

2025-11 PaddleFormers monthly summary: Delivered data-parallel training support for Mixture of Experts (dp-moe) within Zero-Cost Checkpointing (ZCC). This enables scalable training for large models by combining expert-parallelism with ZCC while preserving memory efficiency. Updated state_dict loading/handling to accommodate expert parallelism and ensure correct weight management during training. The change is tracked in commit 6f0c3e6e0be41ac33e0478fdd545dd6692ddc175 ([fea] support dp-moe for zcc and global_expert_id (#2812)).
2025-11 PaddleFormers monthly summary: Delivered data-parallel training support for Mixture of Experts (dp-moe) within Zero-Cost Checkpointing (ZCC). This enables scalable training for large models by combining expert-parallelism with ZCC while preserving memory efficiency. Updated state_dict loading/handling to accommodate expert parallelism and ensure correct weight management during training. The change is tracked in commit 6f0c3e6e0be41ac33e0478fdd545dd6692ddc175 ([fea] support dp-moe for zcc and global_expert_id (#2812)).
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