Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation

Jie Wang; Shiwei Luo; Qi Zhang; Yuanbin Wu

cs.CLOct 5, 2026

Byte Language Models: Scaling, Emergent Abstractions, and Information Allocation

Authors: Jie Wang, Shiwei Luo, Qi Zhang, Yuanbin Wu

Organizations: School of Computer Science, East China Normal University, Shanghai, China · School of Computer Science, Fudan University, Shanghai, China

Abstract

Tokenizer-free language models remove the inductive bias of fixed tokenizers by modeling text directly as bytes, but the resulting longer sequences substantially increase computation and eliminate explicit text abstractions. We ask whether this additional computation can be useful, and whether standard Transformers can learn the abstractions that tokenization provides. We study these questions on Transformers without specialized tokenization-related architectures. With token-superposition training and hash embeddings, byte Transformers consistently outperform subword Transformers as model size scales. We further find that byte Transformers build local text abstractions as external tokenizers: a set of segmentation-like positions are used to collect local context representations, and restricting up to $25\%$ of intermediate layers to these local representations preserves downstream performance. Finally, these learned structures induce highly non-uniform generation difficulty, with uncertainty concentrated near local structure boundaries; exploiting them for speculative decoding yields $3.4\times$ more accepted tokens than in subword Transformers.

Figures & tables

Figure 1: Pareto frontiers of compute-optimal models. (a) Byte has lower optimal BPB

L^{\star}

at the same optimal parameter count

N^{\star}

, while (b) Subword has lower

L^{\star}

at the same training compute

C

. (c) Byte optima generally allocate more source bytes per parameter. Markers indicate fitted optima at the evaluated compute budgets (see details in Appendix C ).

Size	Subword	Byte	Subword	Byte	Subword	Byte
	CUTE (%; $\uparrow$ )		OCRBench (%; $\uparrow$ )		TextVQA (%; $\uparrow$ )
200M	69.89	99.19	27.10	35.10	40.62	45.08
400M	70.78	99.18	27.40	37.40	43.82	48.37
700M	73.04	99.52	29.90	34.50	45.34	50.72
1B	76.44	99.56	31.50	39.40	47.50	52.36
3B	80.83	99.93	34.70	42.40	50.76	55.39

Table 1: Byte consistently outperforms Subword on fine-grained tasks across model scales.

Figure 2: Compute scaling with shared depth and sparse experts. Four-pass Subword closes most of the BPB gap to Byte with higher QA RC, while sparse MoE lowers Byte BPB with less activated computation. Markers are observations and curves are fits.

Figure 3: Byte -1B attention maps during copying. Rows denote copied target bytes and columns denote source bytes from the previous occurrence; each map shows target-to-source attention. All test strings have internal spaces removed. Blue boxes indicate original word chunks, and blue dashed lines mark their final bytes. In intermediate layers, attention concentrates near chunk boundaries, and multiple subsequent target bytes repeatedly attend to the same aggregated positions, indicating the formation of meaningful local representations.

Figure 4: Performance retention under frozen layer intervention. Each cell shows downstream performance relative to the uncompressed baseline for a compressed layer interval $G=[a,b]$ . Byte results are shown in the top row and Subword results in the bottom row. 100% indicates performance at or above the uncompressed baseline.

Figure 5: Token-span averaging largely closes the BPB distribution gap between Byte and Subword . The BPB distribution of Byte has more near-zero and high-BPB mass. All panels are byte-weighted. The dotted curve averages fixed Byte losses over Subword token spans, preserving mean BPB on complete spans (Appendix F.3 ).

Model	$F_{X}(10^{-3})$	$F_{X}(0.1)$	$S_{X}(4)$	$S_{X}(8)$
Ours Byte -1B	23.3	56.6	7.43	0.822
BLT-1B	25.7	59.2	6.62	0.730
H-Net 1-stage XL	22.5	55.8	7.53	0.878
H-Net 2-stage XL	23.1	56.2	7.48	0.867
Ours Subword -1B	0 1.5	16.3	2.35	0.218
Llama-3.1-8B	0 6.5	27.2	1.18	0.090

Table 2: Byte models place more probability mass near zero and at high BPB in their native distributions (Section 4.1 ). Values are percentages of retained text bytes. Figure 11 shows the full BPB distributions.

	200M draft model		50M draft model
	Byte	Subword	Byte	Subword
Acceptance rate (%)	90.6	74.1	84.6	62.0
Accepted tokens / forward	9.527	2.831	5.471	1.618
Byte / Subword ratio	$3.366\times$		$3.381\times$
Bytes / accepted token	1.000	3.364	1.000	3.159
Accepted bytes / forward	9.527	9.523	5.471	5.111

Table 3: Higher byte acceptance rates offset shorter token spans at both draft sizes, giving comparable accepted text per target forward pass. Targets are same-family 1B models, evaluated by an oracle simulation on the given evaluation text (Appendix G.1 ). Forward denotes a simulated target verification pass; counts include only accepted draft tokens (Appendix G.2 ).

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Byte	Subword
Vocabulary size	256	49,152
Convolution kernel width	16	4
TST bag size / duration	4 / first 30% of updates
Hash $n$ -gram lengths $\mathcal{G}$	$\{5,6,7,8\}$	$\{2\}$
Hash tables $\times$ slots per table	$4\times 32{,}768$	$1\times 131{,}072$

Appendix

Table 4: Architectural differences between Byte and Subword . TST and hash settings apply when the corresponding components are enabled.

Size	Layers	Width $d$	FFN	Head dim. GQA/GDN	GQA q/kv heads	GDN qk/v heads	Byte $N$	Subword $N$
200M	16	1,024	2,560	128/64	8/2	8/16	0.207	0.256
400M	20	1,280	3,200	128/64	10/2	10/20	0.374	0.436
700M	24	1,536	3,840	128/64	12/2	12/24	0.644	0.719
1B	28	1,792	4,480	128/64	14/2	14/28	1.022	1.109
3B	40	2,560	6,400	256/128	10/2	10/20	3.181	3.305

Appendix

Table 5: Dense configurations and active parameter counts

N

(billions). In the head-count columns, kv indicates equal key and value counts; qk indicates equal query and key counts.

Size	Layers	Expert width	Byte $N$	Byte $N_{\mathrm{total}}$	Subword $N$	Subword $N_{\mathrm{total}}$
200M	16	256	0.180	0 0.885	0.230	0 0.985
400M	20	384	0.395	0 2.047	0.457	0 2.172
700M	24	384	0.604	0 2.983	0.679	0 3.132
1B	28	512	1.044	0 5.361	1.131	0 5.535
3B	40	768	3.146	16.358	3.269	16.607

Appendix

Table 6: MoE configurations and parameter counts (billions).

Hyperparameter	Byte	Subword
Context length (tokens)	8,192	2,048
Global batch (tokens)	1,048,576	262,144
Base learning rate $\eta_{0}$	0.005
Optimizers	Muon (matrices), AdamW (other parameters)
Scheduler	WSD, 1% warmup, 20% 1-sqrt decay
AdamW $(\beta_{1},\beta_{2})$	$(0.8,0.95)$

Appendix

Table 7: Default pretraining hyperparameters for the main model suite.

Figure 6: TST+hash lowers fitted optimal BPB in both families at shared budgets, with larger gains for bytes. Dots are measurements; curves are fixed-budget slices of

L(M,V)

, and stars are fitted optima. Colors denote training FLOP budgets.

Figure 7: At matched

N,V

, Byte TST+hash improves over raw Subword as source-byte exposure grows. Here,

N

excludes the output embedding to align parameter counts between the two model families. Panels (a–b) show fitted BPB, and (c) shows their difference. Stars are fitted compute-optimal allocations; black dots are training configurations, and dashed contours mark

10^{19}

FLOPs. Surfaces cover only the sampled parameter–data regions.

Recipe	$A$	$\alpha$	$B$	$\beta$	$E$
Byte raw	1054.9	0.42293	13257	0.50811	0.87358
Byte hash	300.19	0.36138	53129	0.57329	0.86467
Byte TST	2547.6	0.46305	3691.2	0.42706	0.83219
Byte TST+hash	550.58	0.39097	9343.3	0.46963	0.81537
Subword raw	2410.6	0.46131	10460	0.48918	0.85900
Subword hash	3810	0.48746	10338	0.48703	0.86609

Appendix

Table 8: Fitted parameters of

L(M,V)=E+AM^{-\alpha}+BV^{-\beta}

(Section 2.2 ).

	200M		400M		700M		1B		3B
Task	Subword	Byte	Subword	Byte	Subword	Byte	Subword	Byte	Subword	Byte
ARC	54.27	53.67	55.56	58.58	59.84	60.49	61.40	62.83	65.81	68.36
MMLU	29.68	29.57	31.09	31.50	32.83	33.63	34.02	35.64	37.08	38.63
CSQA	53.24	52.50	57.08	54.71	57.08	60.11	62.24	61.34	64.62	69.12
HellaSwag	47.24	51.32	52.48	56.91	57.14	62.17	60.94	65.56	67.84	72.89
WinoGrande	51.78	52.72	53.83	55.17	55.88	59.19	58.01	58.96	60.14	62.98

Appendix

Table 9: QA RC scores (%;

\uparrow

) for our models.

	200M		400M		700M		1B		3B
Task	Subword	Byte	Subword	Byte	Subword	Byte	Subword	Byte	Subword	Byte
Spelling	51.70	99.80	56.10	99.80	60.00	99.90	68.60	99.80	81.80	100.00
Spelling (random)	99.10	99.90	99.00	99.90	99.40	99.00	99.60	100.00	99.80	100.00
Inverse spelling	59.90	100.00	63.20	100.00	73.90	100.00	78.30	100.00	82.00	100.00
Inverse spelling (random)	87.30	100.00	93.00	99.90	91.20	100.00	95.60	100.00	97.60	100.00
Character containment	67.20	99.20	70.60	99.60	72.60	100.00	74.00	99.80	78.60	100.00

Appendix

Table 10: CUTE scores (%;

\uparrow

) for our main models.

Task	Final question	Reference answer
Spelling (random)	Question: Spell out the word "bdj".	b d j
Inverse spelling	Question: Write the word "t h e".	the
Character insertion	Question: Add an "l" after every "t" in "little".	litltlle
Word swapping	Question: Swap "is" and "fun" in "It is fun.".	It fun is.

Appendix

Table 11: CUTE evaluation prompt and task examples. The spelling prompt is shown in full; the additional examples show only the final question. Reference answers are displayed separately from the model input.

Benchmark / task	Question	Reference answer
OCRBench Irregular text	What is written in the image?	PARLIAMENT
OCRBench Handwriting	What is written in the image?	strictures
OCRBench Non-semantic text	What is written in the image?	ntishgcwi
TextVQA Scene text QA	What does the small white text spell?	copenhagen
TextVQA Scene text QA	What is the license plate number of this vehicle?	aj52uyv
TextVQA Scene text QA	What is the phone number listed to rent this billboard?	648-3004

Appendix

Table 12: OCRBench and TextVQA examples with questions and reference answers. Images are omitted.

		OCR & document understanding				Knowledge-intensive visual reasoning		General visual understanding
Size	Model	DocVQA	OCRBench	TextVQA	ChartQA	ScienceQA-IMG	MMBench	MME	GQA	POPE
200M	Subword	25.98	27.10	40.62	24.04	42.49	0 8.16	1164.20	48.26	85.37
200M	Byte	28.56	35.10	45.08	26.56	37.88	0 2.06	1187.69	46.26	84.65
400M	Subword	29.77	27.40	43.82	28.00	46.01	23.11	1245.94	49.67	82.28
400M	Byte	32.64	37.40	48.37	28.72	48.19	13.48	1206.10	49.13	82.95
700M	Subword	31.35	29.90	45.34	30.08	50.17	29.98	1332.91	51.87	85.39

Appendix

Table 13: Vision–language evaluation for dense models.

		OCR & document understanding				Knowledge-intensive visual reasoning		General visual understanding
Size	Model	DocVQA	OCRBench	TextVQA	ChartQA	ScienceQA-IMG	MMBench	MME	GQA	POPE
200M	Subword	25.87	26.00	39.79	23.04	50.37	0 2.66	1219.18	46.10	82.26
200M	Byte	25.85	32.90	43.22	23.28	39.96	0 1.37	1154.95	43.81	81.36
400M	Subword	28.68	27.10	44.01	23.48	44.97	0 6.35	1265.12	47.06	84.78
400M	Byte	30.84	35.90	49.61	27.32	47.30	18.21	1292.77	47.61	84.75
700M	Subword	34.26	33.40	49.28	34.48	52.85	35.57	1335.96	53.65	85.17

Appendix

Table 14: Vision–language evaluation for MoE models.

Figure 8: In the main model suite, dense Byte achieves lower BPB and higher QA RC than depth-matched dense Subword , with greater training compute. BPB and QA RC use base models, and CUTE uses SFT models. Points are observations; curves summarize trends within each family’s observed range (Appendix D ).

Figure 9: Ablations on separators: Local retrieval persists with alternative separators. Layer-24 Byte -1B maps use the copying probe in Figure 3 , with words separated by spaces, | , ; , or and . Dashed lines mark original word ends; colors share the same source-normalized attention scale.

Figure 10: Additional performance retention results under the frozen layer intervention. Each cell reports downstream performance relative to the uncompressed baseline for a compressed layer interval $G=[a,b]$ . The axes denote the start layer $a$ , end layer $b$ , and interval length $|G|=b-a+1$ . Byte results are shown in the top row and Subword results in the bottom row. 100% indicates performance at or above the uncompressed baseline.

Figure 11: Byte models show more near-zero and high-BPB mass than subword models. All panels are byte-weighted.

Model	Near-zero BPB	$\alpha$	Tail BPB	$\beta$
Ours Byte -1B	$[5.4\times 10^{-5},2.15]$	-0.946	$[3,15.5]$	1.898
BLT-1B	$[5.4\times 10^{-5},2.86]$	-0.952	$[3.5,15.5]$	1.855
H-Net 1-stage XL	$[4.1\times 10^{-5},5.07]$	-0.916	$[5.5,16]$	1.867
H-Net 2-stage XL	$[3.1\times 10^{-5},5.07]$	-0.918	$[5.5,16.5]$	1.899
Ours Subword -1B	$[4.0\times 10^{-4},0.164]$	-0.612	$[3.5,13]$	1.684
Llama-3.1-8B	$[1.3\times 10^{-5},0.164]$	-0.696	$[2.5,11.25]$	1.407

Appendix

Table 15: Finite-range density fits

f_{X}\propto x^{\alpha}

near zero and

f_{X}\propto e^{-x/\beta}

in the tail, with

\beta

in BPB. Byte exponents are nearer

-1

; tail scales are comparable across granularities.

Figure 12: Near-zero power laws and exponential tail cores describe finite BPB ranges across models. All panels use byte-weighted distributions. Dashed lines show fits, and shading marks fitting ranges. Local exponents

\alpha_{\rm local}

and decay scales

\beta_{\rm local}

indicate where the approximations hold. Dotted tail segments indicate sparse data. Fitting details are in Appendix F.2 .

Figure 13: Token-span averaging reduces near-zero and high-BPB mass for both Byte -1B and BLT-1B. All panels are byte-weighted. Tokenizers yield similar distributions; uniform 4-byte grouping leaves more near-zero mass and less far-tail mass (Appendix F.3 ).

Figure 14: Tokenizers share most boundaries, consistent with their similar aggregation curves. Values are boundary Jaccard similarities (%).

Figure 15: Small drafts achieve high acceptance rates on low-BPB byte tokens. Both families use 16-layer drafts and same-family 1B targets with verification on the given evaluation text within native windows. Left: acceptance rate within each target-BPB interval. Right: target-BPB distributions split into accepted (dark) and rejected (light) tokens, with each native token weighted equally. Colors identify model families.

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Speculative Decoding LLM Inference Acceleration Byte-Level Language Model Language Modeling Diffusion Language Model Decoding Diffusion Language Models

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Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limited by slow, byte-by-byte autoregressive generation. We address this bottleneck in the Byte Latent Transformer (BLT) through new training and generation techniques. First, we introduce BLT Diffusion (BLT-D), a new model and our fastest BLT variant, trained with an auxiliary block-wise diffusion objective alongside the standard next-byte prediction loss. This enables an inference procedure that generates multiple bytes in parallel per decoding step, substantially reducing the number of forward passes required to generate a sequence. Second, we propose two extensions inspired by speculative decoding that trade some of this speed for higher generation quality: BLT Self-speculation (BLT-S), in which BLT's local decoder continues generating past its normal patch boundaries to draft bytes, which are then verified with a single full-model forward pass; and BLT Diffusion+Verification (BLT-DV), which augments BLT-D with an autoregressive verification step after diffusion-based generation. All methods may achieve an estimated memory-bandwidth cost over 50% lower than BLT on generation tasks. Each approach offers its own unique advantages, together removing key barriers to the practical use of byte-level LMs.

Abstract

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