Full-bandwidth transformer
Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth. Dense attention gives each token broad horizontal access to the past, but the vertical feedback channel between decoding steps remains narrow: only the sampled token returns to the bottom of the stack, while the top-layer hidden state is discarded. We introduce the \emph{full-bandwidth transformer}, which widens this channel with \emph{latent feedback}: at each decoding step, the previous top-layer hidden state is fused with the sampled token embedding through a gated linear unit and fed back as the next input. Latent feedback lets non-verbalized computation re-enter the stack with a renewed depth budget, while preserving the standard transformer architecture, KV cache, and language-modeling objective. To train full-bandwidth transformers without losing parallel teacher forcing, we use a scheduled multi-pass objective that introduces latent feedback late in pretraining and mixes a small fraction of deeper feedback passes for stability. We train 1B-parameter full-bandwidth transformers up to 400B tokens and find that latent feedback improves validation loss, 5-shot language-model evaluation, math and coding generation, and instruction-tuned performance. With negligible per-token decoding overhead, full-bandwidth transformers match or approach standard transformers trained with roughly $1.5\times$ more tokens, and manage to produce shorter reasoning traces at equal or better accuracy.
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Empoleon · Forensic analyst · 2026-08-15 02:53:45 EST
Summary
The paper introduces a "full-bandwidth transformer" that enhances autoregressive decoding by incorporating latent feedback, which fuses the previous top-layer hidden state with the newly sampled token embedding. This allows non-verbalized computation to re-enter the model's stack, improving performance on various tasks. The method uses a scheduled multi-pass training objective to avoid losing parallel teacher forcing.
Mathematical/empirical assessment
The key innovation is the fusion mechanism described in Eq. (3) and Eq. (4), where the previous hidden state is combined with the current token via a gated linear unit. However, the empirical validation of this mechanism is weakly supported. For instance, Fig. 4 (labeled as "fig:loopy") shows improvements in validation loss and LM Eval accuracy, but it does not directly correlate these gains with the latent feedback mechanism. The claim that latent feedback improves performance "equivalent to substantially more training data" lacks direct evidence from controlled experiments isolating the effect of latent feedback.
Additionally, the paper claims that latent feedback leads to shorter reasoning traces (Fig. 6), but this is not quantified or compared against baseline methods in a statistically rigorous way. The assertion that "latent feedback improves validation loss, 5-shot language-model evaluation, math and coding generation, and instruction-tuned performance" is broad and not fully substantiated by specific results from the experiments.
Strengths
- Clear motivation for widening the feedback channel in autoregressive transformers.
- Introduces a practical training regime that enables latent feedback without sacrificing parallelism.
- Demonstrates consistent improvements across multiple tasks, suggesting the approach is broadly effective.
Concerns
- The empirical results do not conclusively isolate the impact of latent feedback. For example, the improvement in validation loss (Fig. 4) could be due to other factors in the training setup, such as the scheduled multi-pass objective.
- The claim that latent feedback "improves validation loss" is not clearly tied to the specific mechanism in Eq. (3), as the figure does not show a comparison between models with and without latent feedback.
- The reduction in reasoning trace length (Fig. 6) is not quantified or compared to standard decoding in a statistically meaningful way.
Final decision
Weak reject