Beyond Token Alignment: Event Completion for Cross-Tokenizer On-Policy Distillation

Jiacheng Liu; Jingwei Song; Qituan Zhang; Siheng Chen; Linfeng Zhang

cs.CLSep 28, 2026

Beyond Token Alignment: Event Completion for Cross-Tokenizer On-Policy Distillation

Authors: Jiacheng Liu, Jingwei Song, Qituan Zhang, Siheng Chen, Linfeng Zhang

Organizations: Shanghai Jiao Tong University · Fudan University

Abstract

On-policy distillation (OPD) transfers knowledge between language models through teacher supervision on student-generated trajectories. With different tokenizers, a single teacher token may require multiple student tokens to generate, creating intermediate states where the event is entered but not yet completed. Existing cross-tokenizer methods align tokens or text spans to construct comparable prediction targets. We study a complementary problem after partial generation: once the student produces a prefix of a teacher token, multiple next tokens may complete the same remaining bytes, but the teacher only specifies the required completion rather than how probability should be divided among these valid continuations. We introduce Event-Set Completion Distillation (ESCD), which complements cross-tokenizer probability alignment with completion-set supervision. ESCD aggregates prefix-related teacher events and supervises the total probability of byte-compatible one-step student completions, avoiding tokenizer-dependent probability splits among individual tokens. The method reuses student trajectories and predictions, requiring neither additional rollouts nor changes to the student vocabulary. Experiments demonstrate consistent gains in mathematics, code, and scientific reasoning across model families and tokenizers, extending to large-scale MoE distillation from a 1T teacher to a 35B student. Local analyses show that retaining completion sets better matches the reference supervision, while one-step completion covers over 99% of observed compatible teacher mass after partial event entry in the studied tokenizer pairs. These findings support event entry and event completion as complementary supervision targets for cross-tokenizer knowledge transfer. Code will be released on GitHub.

Figures & tables

Figure 1: Cross-tokenizer distillation results across model scales. Left: Teacher-normalized pass@ $n$ scores on six mathematics and code benchmarks, with Qwen3.5-2B distilled from Qwen3-32B or GLM-Z1-9B. Recovery is the Student score divided by the corresponding teacher score, expressed as a percentage; 100% indicates matching teacher performance. We use $n=8$ for mathematics and $n=2$ for code. Right: Absolute scores on FrontierScience Olympiad and PHYRD-40 for two large-scale heterogeneous MoE pairs. Together, these results show consistent improvements across task domains, model families, and scales.

Method	FrontierScience Olympiad-100	PHYRD-40
Qwen3.5-397B-A17B $\dagger$	70.0	79.8
Qwen3-30B-A3B-Thinking (base)	48.0	43.0
BPM	48.0	46.6
Ours	51.0	49.6
$\Delta$ vs. best baseline	+3.0	+3.0
Kimi-K2.7-Code $\dagger$	75.0	83.6

Table 2: Cross-tokenizer OPD on heterogeneous MoE pairs with 397B and 1T teachers. In the Kimi setting, BPM and ESCD use the same SFT-initialized checkpoint due to direct OPD instability.

Teacher $\rightarrow$ Student	Root	Single	Event	$\bm{\Delta}$
Qwen3 $\rightarrow$ Qwen3.5	0.7383	0.4323	0.8495	+0.4172
GLM-Z1 $\rightarrow$ Qwen3.5	0.8848	0.7493	0.9341	+0.1848

Table 3: Local target comparison on contexts satisfying the COUF diagnostic criteria. We report reference-energy-weighted state-space COUF;

\Delta

denotes Event minus Single.

Teacher $\rightarrow$ Student	Observed	1-step	Deeper
Qwen3 $\rightarrow$ Qwen3.5	81.83%	99.01%	0.99%
GLM-Z1 $\rightarrow$ Qwen3.5	83.29%	99.43%	0.57%

Table 4: Full-vocabulary child-supervision availability. Observed is relative to branchable teacher mass; 1-step and Deeper characterize representative residuals within visited mass.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Model	Recognized termination	Preferred bridge stop
Qwen3 / Qwen3.5	model-declared EOS / stop tokens	<\|im_end\|>
GLM-Z1	<\|endoftext\|> and configured stops	<\|endoftext\|>
Kimi-K2.7-Code	[EOS] , <\|im_end\|>	<\|im_end\|>

Appendix

Table 5: Model-specific termination handling. The bridge stop is the token used to represent a normally completed response after cross-tokenizer retokenization.

Setting	Small-scale	Large-scale
Teacher	Qwen3-32B / GLM-Z1-9B	Qwen3.5-397B-A17B	Kimi-K2.7-Code
Student	Qwen3.5-2B	Qwen3-30B-A3B-Thinking	Qwen3.6-35B-A3B
Training framework	Slime	Slime	Slime
Student optimization	Megatron-LM	Megatron-LM	Megatron-LM
Rollout and Teacher serving	SGLang	SGLang	SGLang
Training data	DAPO-Math + TACO	SciDeriv	SciDeriv

Appendix

Table 6: Training configurations for OPD experiments. GPU totals include Teacher serving, Student optimization, and Student rollout, excluding separately scheduled evaluation jobs.

Task	Mathematics	Code
Source	DAPO-Math	TACO
Prompts	10,000	10,000

Appendix

Table 7: Training data for small-scale distillation.

Task	Mathematics	Code
Source	DAPO-Math	TACO
Prompts	10,000	10,000

Appendix

Table 7: Training data for small-scale distillation.

Task type	Prompts
Conclusion assessment	10,000
Conclusion reconstruction	7,577
Total	17,577

Appendix

Table 8: Composition of SciDeriv for large-scale distillation.

Evaluation setting	Context	Max output	Temp .	Top- $p$	Top- $k$	Samples/problem
AIME24/25/26	$32{,}768$	$27{,}648$	$0.6$	$0.95$	$20$	$8$
HMMT-26	$32{,}768$	$27{,}648$	$0.6$	$0.95$	$20$	$8$
LiveCodeBench	$32{,}768$	$27{,}648$	$0.6$	$0.95$	$20$	$2$
TACO	$32{,}768$	$27{,}648$	$0.6$	$0.95$	$20$	$2$
FrontierScience Olympiad	$262{,}144$	$256{,}000$	$1.0$	$0.95$	$20$	$1$
PHYRD-40	$262{,}144$	$256{,}000$	$1.0$	$0.95$	$20$	$1$

Appendix

Table 9: Generation settings for the controlled and large-scale experiments. Context and output limits are measured in tokens; the context limit includes both the input prompt and generated response.

Tokenizer pair	$\mathbf{\|V_{T}\|}$	$\mathbf{\|V_{S}\|}$	$\mathbf{M}$	$\mathbf{C_{T}}$	$\mathbf{C_{S}}$	$\mathbf{J}$
Qwen3 $\rightarrow$ Qwen3.5	151,669	248,077	131,612	86.78%	53.05%	49.08%
Qwen3.5 $\rightarrow$ Qwen3	248,077	151,669	131,612	53.05%	86.78%	49.08%
GLM-Z1 $\rightarrow$ Qwen3.5	151,343	248,077	142,628	94.24%	57.49%	55.54%
GLM-Z1 $\rightarrow$ Qwen3	151,343	151,669	123,118	81.35%	81.18%	68.44%
Kimi-K2.7 $\rightarrow$ Qwen3.6	163,840	248,077	121,757	74.31%	49.08%	41.96%

Appendix

Table 10: Static shared-vocabulary overlap under the deterministic one-to-one canonical-surface mapping. Vocabulary sizes exclude checkpoint padding.

Teacher token	$q_{T}(v\mid x)$	Student tokenization	Residual after Br
Bread	$0.4$	Br , ead	ead
Breads	$0.2$	Br , eads	eads
Break	$0.3$	Br , eak	eak
Cat	$0.1$	Cat	—

Appendix

Table 12: Illustrative teacher predictions and student realizations. Residuals are shown for individual teacher tokens after Br , before ESCD aggregation; Cat is incompatible with this action.

Teacher $\rightarrow$ Student	Contexts	Matched mass	Both 1-step
Qwen3 $\rightarrow$ Qwen3.5	32	0.8745%	0
GLM-Z1 $\rightarrow$ Qwen3.5	37	0.0662%	0

Appendix

Table 13: Available comparisons between visited and alternative child states. The last column counts contexts where both branches admit one-step completion of the same teacher event.

Teacher $\rightarrow$ Student	Root	Counterfactual	Visited
Qwen3 $\rightarrow$ Qwen3.5	0.999031	0.999982	0.999032
GLM-Z1 $\rightarrow$ Qwen3.5	0.998486	0.999997	0.998489

Appendix

Table 14: State-space COUF when the counterfactual branch may use deeper completion. The teacher event and root supervision are matched, but completion depth is not controlled.

Pair	$\bm{m_{g}}$	$\bm{b_{g}}$	Mass share	Child visited	One-step given visit
Qwen3 $\rightarrow$ Qwen3.5	$1$	$1$	0.0026%	34.5351%	96.0012%
	$1$	$>1$	0.1089%	70.6500%	71.9885%
	$>1$	$1$	88.2125%	80.9535%	99.9991%
	$>1$	$>1$	11.6760%	88.6045%	92.4118%
	Overall		100.0000%	81.8344%	99.0135%
GLM-Z1 $\rightarrow$ Qwen3.5	$1$	$1$	2.3143%	88.8103%	99.9993%

Appendix

Table 15: Root event structure and completion availability from the full teacher vocabulary after exact-shared and invalid-token filtering. Statistics use aligned, non-whitespace positions in 16 frozen student trajectories per pair, grouped by

m_{g}

and

b_{g}

teacher $\rightarrow$ Student	Events	Set size	Full prob.	Crossing prob.
Qwen3 $\rightarrow$ Qwen3.5	48	35.28	0.8846	2.10%
GLM-Z1 $\rightarrow$ Qwen3.5	287	14.05	0.9412	0.63%

Appendix

Table 16: Boundary-crossing ambiguity on materialized natural-child events. “Set size” is weighted by teacher source mass; “Crossing prob.” is the fraction of completion-event probability assigned to tokens that extend beyond the residual boundary.

Teacher $\rightarrow$ Student	Projection	Energy ratio	Cosine
Qwen3 $\rightarrow$ Qwen3.5	1.0566	1.1550	0.9832
GLM-Z1 $\rightarrow$ Qwen3.5	1.0302	1.0857	0.9887

Appendix

Table 17: Local target-gradient sensitivity to removing boundary-crossing completions.

Teacher $\rightarrow$ Student	Position upper bound	Observed	Top-1 observed
Qwen3 $\rightarrow$ Qwen3.5	93.47%	81.83%	71.55%
GLM-Z1 $\rightarrow$ Qwen3.5	95.49%	83.29%	76.23%

Appendix

Table 18: Compatible-child visitation under full-vocabulary analysis. All columns are relative to the same pooled branchable teacher mass. The position upper bound counts all branchable mass at positions with any compatible sampled action; Top-1 ranks branches by teacher source mass.

Teacher $\rightarrow$ Student	1-step	Deeper
Qwen3 $\rightarrow$ Qwen3.5	99.01%	0.99%
GLM-Z1 $\rightarrow$ Qwen3.5	99.43%	0.57%

Appendix

Table 19: One-step completion availability for representative residual constraints, weighted by teacher mass and conditional on a compatible child being visited.

Step	Mean tokens	Median tokens	Max tokens
1	7,698	7,550	14,470
16	10,587	7,605	50,590
20	3,022	2,518	8,099
26	247	24	1,818
30	56	8	566
32	8	6	20

Appendix

Table 20: Response-length collapse in the truncation-filtered Kimi-to-Qwen BPM run. Statistics describe the 16 responses retained at each listed step, rounded to the nearest token.

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On-policy distillation (OPD) is a standard tool for transferring teacher behavior to a smaller student, but it implicitly assumes that teacher and student predictions are comparable token by token, an assumption that fails whenever the two models tokenize the same text differently. Under heterogeneous tokenizers, exact shared-token matching silently discards a large fraction of the teacher signal at precisely the positions where vocabularies disagree. We propose \textbf{\underline{Sim}ple \underline{C}ross-\underline{T}okenizer OPD (SimCT)}, which restores this signal by enlarging the supervision space: alongside shared tokens, SimCT compares teacher and student over short multi-token continuations that both tokenizers can realize, leaving the OPD loss form itself unchanged. We show that these units are the finest jointly tokenizable supervision interface, and that coarser alternatives remove teacher-student distinctions that are useful for on-policy learning. Across three heterogeneous teacher-student pairs on mathematical reasoning and code-generation benchmarks, SimCT shows consistent gains over shared-vocabulary OPD and representative cross-tokenizer baselines, with ablations confirming that the improvements come from recovering supervision discarded by exact shared-token matching. Code is available at \href{https://github.com/sunjie279/SimCT-}{https://github.com/sunjie279/SimCT-}.

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