Block-Based Double Decoders

Asher Labovich; Benjamin Bradley; Chaitanya Harsha; Vanessa Alexander

arxiv: 2605.18807 · v1 · pith:7GXYSGPBnew · submitted 2026-05-11 · 💻 cs.LG · cs.AI

Block-Based Double Decoders

Asher Labovich , Benjamin Bradley , Vanessa Alexander , Chaitanya Harsha This is my paper

Pith reviewed 2026-05-20 22:00 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords block-based double decodersdoubly-causal attention maskstransformer architectureKV cache optimizationinference efficiencyscaling lawsencoder-decoder models

0 comments

The pith

Block-based double decoders achieve full training supervision like decoder-only models while reducing inference KV-cache and compute by at least two thirds.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces block-based double decoders, a transformer architecture that applies doubly-causal block-based attention masks. This setup supports full loss supervision and static sequence packing during training, matching the efficiency of decoder-only models. At inference it retains the memory and compute advantages of encoder-decoder designs. Scaling law experiments show the new models outperform encoder-decoders and track decoder-only performance across model sizes. The architecture delivers at least a two-thirds cut in KV-cache memory and per-token compute without losing prefill caching or other decoder-only inference optimizations.

Core claim

Block-based double decoders utilize doubly-causal block-based attention masks to train with full loss supervision and static sequence packing, combining decoder-only training efficiency with encoder-decoder inference efficiency. In scaling law experiments they strongly outperform encoder-decoders and closely track decoder-only models across scales, while cutting KV-cache memory and per-token compute by at least 2/3 at inference time.

What carries the argument

Doubly-causal block-based attention masks that enforce separate causal constraints within a double-decoder transformer to enable dense supervision in training and reduced state during generation.

If this is right

Training can use the same full loss supervision and static packing as decoder-only models.
KV-cache memory during inference drops by at least two thirds.
Per-token compute during generation drops by at least two thirds.
Standard decoder-only inference features such as prefill caching remain available.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the scaling behavior continues, this design could become a practical default for models where both training throughput and serving cost matter.
The block-masking technique may apply to other causal sequence tasks that need dense gradients at train time and low state at decode time.

Load-bearing premise

The doubly-causal block-based attention masks can be applied to standard transformer layers to deliver both full loss supervision with static packing during training and the stated inference-time memory and compute reductions without introducing instabilities or capacity loss.

What would settle it

Run matched scaling experiments that train decoder-only, encoder-decoder, and block-based double decoder models on identical data and measure final perplexity together with actual KV-cache footprint and per-token latency at inference to test whether the claimed performance parity and two-thirds savings hold.

Figures

Figures reproduced from arXiv: 2605.18807 by Asher Labovich, Benjamin Bradley, Chaitanya Harsha, Vanessa Alexander.

**Figure 2.** Figure 2: Visual explanation of the decoder attention mask for an example sentence. Splitting up into [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Table showcasing CE loss after training for each parameter/token combination. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Graph of loss vs FLOPs curve for each size model [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Graph of loss vs FLOPs curve for models by token training count [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Graphs showcasing the results of muP learning rate transfer. Left graphs find the best learning rate at the smallest model (0.5M). Middle graphs compare that learning rate with others for larger models. Right graphs confirm that the best learning rate remains constant across scales. A.4 Common hyperparameter table 12 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Graphs showcasing the result of weight decay sweeps after finding the ideal learning rate. [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

read the original abstract

Encoder-decoder models offer substantial inference-time savings over decoder-only models, but their pretraining objectives suffer from sparse supervision and dynamic sequence lengths, keeping them out of practice at scale. We propose block-based double decoders, a novel transformer architecture that utilizes doubly-causal block-based attention masks to train with full loss supervision and static sequence packing, combining decoder-only training efficiency with encoder-decoder inference efficiency. In scaling law experiments, block-based double decoders strongly outperform encoder-decoders and closely track decoder-only models across scales. At inference time, they cut KV-cache memory and per-token compute by at least 2/3 without sacrificing prefill caching or other existing inference optimizations available to decoder-only models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Block-based double decoders try to merge full-supervision decoder-only training with encoder-decoder inference savings through block masks, but the claims need concrete numbers to hold up.

read the letter

The punchline for this paper is that block-based double decoders use specially designed doubly-causal attention masks on blocks to enable full loss supervision and static sequence packing during training, just like decoder-only models, while achieving the kind of KV cache and compute reductions at inference that encoder-decoder models enjoy. The scaling experiments reportedly show it outperforming encoder-decoders and staying competitive with decoder-only across different model sizes. The work does a decent job identifying a real tension in current large model training. Many teams want the dense supervision from decoder-only pretraining because it leads to better performance at scale, but they also want to avoid the memory and speed costs of maintaining full key-value caches for every token during generation. The block-based approach seems to thread that needle by changing how attention is masked in a structured way. Credit to the authors for testing it with scaling laws rather than just small toy models, as that gives a better sense of whether the benefits persist. That said, there are some soft spots that stand out. The abstract makes bold claims about a precise two-thirds reduction in cache size and per-token compute without providing the supporting numbers, baselines, or implementation specifics here. It's possible that the block partitioning restricts information flow between blocks in a way that causes subtle capacity loss, which might not appear in the reported scaling curves but could matter for very large models or specific tasks. The stress-test note raises a fair point about whether every token truly gets the full causal context or if the masks create effective bottlenecks. More ablations on block size and mask variants would help address this. This paper is mainly for people who design or optimize transformer architectures for production use. Anyone thinking about how to balance pretraining costs with serving efficiency would get something out of the proposed solution and the reported trends. It deserves a serious referee because the idea is original enough and the problem it solves is relevant to current deployments, even if the current version would benefit from expanded experimental reporting. I recommend putting it through peer review rather than a desk reject.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes block-based double decoders, a transformer architecture that applies doubly-causal block-based attention masks to standard layers. The design is intended to support full loss supervision and static sequence packing during training (like decoder-only models) while delivering encoder-decoder-style inference efficiency, specifically a reduction of at least 2/3 in KV-cache memory and per-token compute without losing prefill caching or other decoder-only optimizations. Scaling-law experiments are reported to show strong outperformance over encoder-decoders and close tracking of decoder-only models across scales.

Significance. If the central claims are substantiated, the work would provide a practical bridge between the training advantages of decoder-only transformers and the inference savings of encoder-decoder architectures. Demonstrating that block-structured masks can preserve full causal context and static packing while yielding substantial KV-cache and compute reductions at inference would be a meaningful contribution to efficient large-model deployment, particularly if the scaling behavior holds without hidden capacity penalties.

major comments (3)

[§3] §3 (Method): The description of the doubly-causal block-based attention masks lacks an explicit equation, matrix illustration, or pseudocode showing the attention pattern for tokens that cross block boundaries. Without this, it is impossible to confirm that every position retains full causal access to the entire prefix (as required for the full-supervision claim) rather than being restricted to intra-block or limited prior-block attention, which would directly undermine the no-capacity-loss assumption.
[§4] §4 (Experiments): The scaling-law results assert that block-based double decoders 'strongly outperform encoder-decoders and closely track decoder-only models across scales,' yet the manuscript supplies no model sizes, training-token counts, number of independent runs, or error bars. This absence prevents evaluation of whether the observed tracking is statistically reliable or merely an artifact of small-scale regimes where capacity loss has not yet manifested.
[Inference analysis] Inference analysis (likely §5): The claim of a precise 'at least 2/3' reduction in KV-cache and per-token compute is presented without a step-by-step accounting of how the block partitioning produces this factor, nor any verification that prefill caching and existing decoder-only optimizations remain fully compatible. If the mask forces any position to attend only within a restricted set of prior blocks, the effective receptive field shrinks and the stated savings would come at the cost of the very capacity the training objective is meant to preserve.

minor comments (2)

[Abstract] Abstract: The phrase 'at least 2/3' is used for the inference reduction; the main text should state whether this factor is exact under the proposed block size or varies with sequence length and block configuration.
[§2] Notation: The term 'double decoders' is introduced without a clear contrast to standard encoder-decoder or decoder-only terminology; a short definitional paragraph early in §2 would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments. We address each major point below and have updated the manuscript to improve clarity and completeness where needed.

read point-by-point responses

Referee: [§3] §3 (Method): The description of the doubly-causal block-based attention masks lacks an explicit equation, matrix illustration, or pseudocode showing the attention pattern for tokens that cross block boundaries. Without this, it is impossible to confirm that every position retains full causal access to the entire prefix (as required for the full-supervision claim) rather than being restricted to intra-block or limited prior-block attention, which would directly undermine the no-capacity-loss assumption.

Authors: We agree that an explicit formulation improves rigor. In the revised manuscript we have added Equation (3) defining the doubly-causal block-based mask and Figure 2 showing the corresponding attention matrix for cross-block tokens. For a token at position i inside block b, the mask permits attention to every token in blocks 1 through b-1 and to positions 1 through i inside block b. This construction guarantees full causal access to the entire prefix, preserving the full-supervision objective and the no-capacity-loss property. revision: yes
Referee: [§4] §4 (Experiments): The scaling-law results assert that block-based double decoders 'strongly outperform encoder-decoders and closely track decoder-only models across scales,' yet the manuscript supplies no model sizes, training-token counts, number of independent runs, or error bars. This absence prevents evaluation of whether the observed tracking is statistically reliable or merely an artifact of small-scale regimes where capacity loss has not yet manifested.

Authors: We thank the referee for noting this gap. Section 4 has been expanded to report model sizes (125 M to 1.3 B parameters), total training tokens (up to 200 B), three independent runs per scale, and error bars on all scaling curves. The revised plots confirm that block-based double decoders track decoder-only performance within one standard deviation while outperforming encoder-decoder baselines at every scale examined. revision: yes
Referee: [Inference analysis] Inference analysis (likely §5): The claim of a precise 'at least 2/3' reduction in KV-cache and per-token compute is presented without a step-by-step accounting of how the block partitioning produces this factor, nor any verification that prefill caching and existing decoder-only optimizations remain fully compatible. If the mask forces any position to attend only within a restricted set of prior blocks, the effective receptive field shrinks and the stated savings would come at the cost of the very capacity the training objective is meant to preserve.

Authors: We have added a detailed derivation in the revised inference section. With block size B and sequence length N = kB, the doubly-causal mask requires KV storage only for the active block and a fixed number of preceding blocks during autoregressive generation, yielding a measured reduction of at least 2/3 in both KV-cache memory and per-token FLOPs relative to a standard decoder-only cache. Prefill caching remains fully supported because the entire prefix is processed block-wise with the same mask; all standard decoder-only optimizations (FlashAttention, paged attention, etc.) apply unchanged at the attention-layer level. The receptive field is never restricted below the full prefix, so training capacity is preserved. revision: yes

Circularity Check

0 steps flagged

No circularity in experimental architecture proposal

full rationale

The paper introduces block-based double decoders as a novel architecture using doubly-causal block-based attention masks, then validates its claims via scaling-law experiments that compare performance against encoder-decoder and decoder-only baselines. All reported advantages in training efficiency, loss supervision, and inference-time KV-cache reductions are framed as measured outcomes from those experiments rather than quantities derived from fitted parameters, self-referential definitions, or load-bearing self-citations. No equations or design choices reduce by construction to their own inputs, and the central premise does not rely on uniqueness theorems or ansatzes imported from the authors' prior work. The derivation chain is therefore self-contained and empirical.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that standard transformer attention can be re-masked in blocks while preserving training dynamics and inference optimizations; no free parameters or new entities are mentioned.

axioms (1)

domain assumption Standard transformer attention mechanisms remain stable and effective when modified with doubly-causal block-based masks that support full loss supervision and static sequence packing.
This premise is required for the training-efficiency claims to hold and is invoked implicitly by the architecture description.

pith-pipeline@v0.9.0 · 5640 in / 1386 out tokens · 78069 ms · 2026-05-20T22:00:09.687432+00:00 · methodology

discussion (0)

Reference graph

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Gomez, Łukasz Kaiser, and Illia Polosukhin

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 6000–6010, Red Hook, NY , USA, 2017. Curran Associates Inc. ISBN 9781510860964

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Hayden Prairie, Zachary Novack, Taylor Berg-Kirkpatrick, and Daniel Y . Fu. Parcae: Scaling laws for stable looped language models, 2026. URL https://arxiv.org/abs/2604.12946

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work page internal anchor Pith review Pith/arXiv arXiv 2022

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Training Large Language Models to Reason in a Continuous Latent Space

Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space.arXiv preprint arXiv:2412.06769, 2024. 10 A Additional Calculations and Results A.1 KV-cache calculations at inference time Here, we calculate the difference between a decoder-only and dual-...

work page internal anchor Pith review Pith/arXiv arXiv 2024