Layer-Feedback Transformer (LFT)

Layer-Feedback Transformers (LFTs) reuse adjacent Transformer blocks during a forward pass, allowing earlier layers to refine representations after they have been processed by deeper layers without adding parameters. In an LFT with N unique layers, the feedback routing produces 3N−4 layer executions, increasing compute by roughly 1.7 to 2.8 times relative to a standard Transformer of the same parameter count. Controlled experiments trained parameter-matched standard and LFT models on 500 million tokens, finding that LFT underperformed at 2.5 million parameters but improved overall benchmark accuracy at 10 million parameters, particularly in context tracking, quantitative tasks, and logical reasoning, and achieved smaller gains at 25 million parameters. Results also indicated that LFT may require longer training to be beneficial, as a 10-million-parameter version trailed after 200 million tokens but exceeded the standard model after 500 million. The comparison was not compute-matched, however, so the reported gains may stem partly from LFT’s additional layer executions rather than feedback routing alone, making the approach most relevant where parameter limits matter more than training and inference cost.

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