---
title: "Training Hybrid Block Diffusion Language Models with Partial Bidirectionality — Stuff That Spins"
description: "arXiv:2607.02805v1 Announce Type: new Abstract: High-throughput long-context generation is one of the central challenges for large language models. Generation …"
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date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:05:04.005987+00:00"
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# Training Hybrid Block Diffusion Language Models with Partial Bidirectionality

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02805  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02805v1 Announce Type: new Abstract: High-throughput long-context generation is one of the central challenges for large language models. Generation is typically memory-bandwidth-bound rather than compute-bound: each decoding step must stream the accumulated key/value (KV) cache from memory, so bandwidth demand grows with context length while only one token is emitted. Two parallel approaches have therefore emerged: reducing memory access with efficient attention variants and linear-ti

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