Backpropagated Output Momentum: Relocating Optimizer History from Parameters to Task Space

arXiv:2609.36738 · cs.LG, cs.CL · Submitted 2026-09-29 · Read on arXiv

cs.LG, cs.CL

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: 53 pages, 10 figures

Code: https://github.com/lyclyq/Optimizor_arxiv

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adam — 59× in this paper · explained in 2 episodes
• Fine-Tuning — 39× in this paper · explained in 5 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Benchmark — 11× in this paper · explained in 2 episodes
• Perplexity — 8× in this paper · explained in 3 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Aggregation — 7× in this paper · explained in 1 episode
• Embeddings — 6× in this paper · explained in 2 episodes
• Backpropagated Output Momentum — 5× in this paper
• Calibration — 4× in this paper · explained in 7 episodes
• Post-Selection — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Generative AI — 3× in this paper · explained in 2 episodes
• Activation Checkpointing — 2× in this paper · explained in 1 episode
• NATURAL-LANGUAGE PROCESSING — 2× in this paper
• Quantization — 2× in this paper · explained in 12 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• General Language Understanding Evaluation — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Riemannian optimization — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Riemannian Adaptive Optimization Methods
• A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
• M+Adam: Low-Precision Training via Additive-Multiplicative Optimization
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
• Qwen3 Technical Report

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