← 研踪 PaperTrailarXiv:2605.07013 · v3
CoBit: Language Modeling with Bitstream Diffusion
首次提交 2026-05-08 06:50
版本更新 2026-11-07 00:59
雷达观测 2026-11-09 13:22
摘要
We introduce CoBit, a continuous diffusion language model that generates text by jointly denoising fixed-width binary token codes. Grouping each code's bits into a single Transformer patch preserves sequence length while replacing vocabulary-wide predictions with $\lceil\log_2 V\rceil$ bit logits. A matched-filter residual parameterization supplies the analytic isolated-bit posterior, leaving the network to learn contextual corrections. Stochastic sampling on an entropy-rate grid provides the largest empirical gain; the grid allocates both solver resolution and effective Langevin correction to noise levels where information is resolved. At 256 denoiser evaluations, CoBit-S (130M parameters) achieves generative perplexity 59.76 on LM1B and 27.06 on OpenWebText, at unigram entropies 4.31 and 5.26, respectively. Scaling to 462M parameters improves OpenWebText generative perplexity to 19.48 at entropy 5.40. Beyond open-ended text generation, CoBit achieves 65.9% exact-match accuracy on hard Sudoku. On GSM8K, score-temperature scaling with an untempered final integration step raises CoBit-S accuracy from 28.1% to 35.3% without additional denoiser evaluations, placing it within the reported range of temperature-tuned discrete diffusion baselines. Matched-trunk profiling also shows lower peak memory and higher throughput than a dense token-output baseline.
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-11-07 00:59 · 最新观测版本
CoBit: Language Modeling with Bitstream Diffusion
We introduce CoBit, a continuous diffusion language model that generates text by jointly denoising fixed-width binary token codes. Grouping each code's bits into a single Transformer patch preserves sequence length while replacing vocabulary-wide predictions with $\lceil\log_2 V\rceil$ bit logits. A matched-filter residual parameterization supplies the analytic isolated-bit posterior, leaving the network to learn contextual corrections. Stochastic sampling on an entropy-rate grid provides the largest empirical gain; the grid allocates both solver resolution and effective Langevin correction to noise levels where information is resolved. At 256 denoiser evaluations, CoBit-S (130M parameters) achieves generative perplexity 59.76 on LM1B and 27.06 on OpenWebText, at unigram entropies 4.31 and 5.26, respectively. Scaling to 462M parameters improves OpenWebText generative perplexity to 19.48 at entropy 5.40. Beyond open-ended text generation, CoBit achieves 65.9% exact-match accuracy on hard Sudoku. On GSM8K, score-temperature scaling with an untempered final integration step raises CoBit-S accuracy from 28.1% to 35.3% without additional denoiser evaluations, placing it within the reported range of temperature-tuned discrete diffusion baselines. Matched-trunk profiling also shows lower peak memory and higher throughput than a dense token-output baseline.
查看此版本解读 →查看此版本原文 ↗