Backpropagation-Free Trunk Training via the Split Forward Gradients

arXiv:2607.16612 · stat.ML, cs.LG · Submitted 2026-07-18 · Read on arXiv

Tian Qin, Wei-Min Huang

stat.ML, cs.LG

Submitted: 2026-07-18

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

Terminology

• Adam — 46× in this paper · explained in 3 episodes
• Perplexity — 29× in this paper · explained in 3 episodes
• Ablation — 14× in this paper · explained in 1 episode
• Preconditioner — 13× in this paper · explained in 2 episodes
• Predictive Coding — 12× in this paper · explained in 3 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Weight Decay — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Core — 3× in this paper · explained in 2 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Augmentation — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Papers — 2× in this paper · explained in 3 episodes
• Performance Gap — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Training Objective — 2× in this paper · explained in 3 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Variance Reduction — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adjoint Method — 1× in this paper · explained in 2 episodes
• Associative Memory — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Continual Learning — 1× in this paper · explained in 3 episodes
• Digital Twin — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Latent Variables — 1× in this paper · explained in 2 episodes
• Memorization — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Sample Efficiency — 1× in this paper · explained in 7 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Gradients without Backpropagation
• Training Deep Nets with Sublinear Memory Cost
• The Forward-Forward Algorithm: Some Preliminary Investigations
• Adam: A Method for Stochastic Optimization
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Large Batch Training of Convolutional Networks
• Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

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