Making, Not Taking, the Best of N
Authors: Ammar Khairi
Daniel D'souza
Marzieh Fadaee
Julia Kreutzer
Venue: ICLR 2026 Poster (2026)
Keywords: Best-of-N
test-time scaling
synthetic data generation
inference
multilingual
diversity
ensembling
Primary area: foundation or frontier models, including LLMs
TL;DR: We introduce Fusion-of-$N$ that synthesizes outputs from $N$ samples rather than selecting one amongst them (Best-of-$N$).

Abstract: Obtaining high-quality generations in modern LLMs has largely been framed as a selection problem: identifying a single winning generation from a diverse pool of $N$ samples, the Best-of-$N$ (BoN).
Yet, this approach is inherently zero-sum, discarding diverse and potentially useful information from the pool. Instead, we explore a collaborative setup, where all candidates can potentially contribute to the final winning generation. To this end, we propose Fusion-of-$N$ (FusioN): a method that uses a general LLM judge to synthesize the most informative elements of each sample into a single final answer. 
We compare FusioN to BoN in two settings, (i) test-time scaling, where we sample and aggregate from a single model at test-time (ii) synthetic data generation, where we fuse samples from a pool of diverse teachers to improve a student model. We extensively benchmark both setups across 11 languages, 3 diverse benchmarks and varying model scales. Across the bench, FusionN consistently outperforms BoN showing versatility and robustness both in test-time scaling and in downstream gains from synthetic data generation. We also perform extensive analysis on FusioN, where it shows surprising strengths and robustness under challenging settings.
These results show that we should shift how we think about evaluating and utilizing LLM generations from a monolithic measure of quality, to embracing their polylithic nature. This shift allows us to integrate diverse strengths, unlock latent potential, and achieve improvements that were previously inaccessible through selection alone.

Published as a conference paper at ICLR 2026
MAKING, NOT TAKING, THE BEST OF N
Ammar Khairi & Daniel D'souza & Marzieh Fadace & Julia Kreutzer Cohere Labs
fammar, juliikkeeetteeee/ccceee....
ABSTRACT
Obtaining high-quality generations in modern LLMs has largely been framed as selection problem: identifying single winning generation from diverse pool of N samples, the Best-of-N (BoN). Yet, this approach is inherently zero-sum, discarding diverse and potentially useful information from the pool. Instead, we explore collaborative setup, where all candidates can potentially contribute to the final winning generation. To this end, propose Fusion-of-NV (FUSION): a method that uses a general LLM judge to synthesize the most informative ele- ments of each sample into single final answer. We compare FUSION BoN two settings, (i) test-time scaling, where we sample and aggregate from single model at test-time (ii) synthetic data generation, where we fuse samples from a pool of diverse teachers to improve student model. We extensively benchmark both setups across 11 languages, diverse benchmarks and varying model scales. Across the bench, FUSION consistently outperforms BoN showing versatility and robustness both in test-time scaling and downstream gains from synthetic data generation. We also perform extensive analysis on FUSION, where it shows prising strengths and robustness under challenging settings. These results show that should shift how we think about evaluating and utilizing LLM generations from a monolithic measure of quality, to embracing their polylithic nature. This shift allows to integrate diverse strengths. unlock latent potential, and achieve improvements that were previously inaccessible through selection alone.
INTRODUCTION
Many of today's advances in LLMs rely heavily aggregation at inference: The dominant ap- proach, Best-of-N (BoN), involves generating multiple candidates and selecting one among them as the final output. This approach has proven highly effective for test-time scaling in tasks ranging from math reasoning and translation to open-ended tasks (Snell al.. 2025; Khairi et al. 2025
Yao al. 2023 Wang et al. 2023a). and for producing synthetie data used in fine-tuning (J (Jayalath
ct al. 2025:
Mucnnighoff et al. 2025), especially in multilingual setups (Grattafiori et al. [024
Dang al.
et al. 2025 Hemnández-Cano et al., 2025 Lai & Nissim, 2024 Hwang
et al.) 2025 Odumakinde et al. 2025 Rei et al.] (025). However, existing aggregation methods treat generations as competitors a zero-sum game. Whether through majority voting (Brown et al., 2024). self-consistency (Wang et al.) 2202a)), or reward-model scoring (Ouyang et al.) 2022), the goal is find the single best answer while discarding the rest. This hard selection step imposes clear limitations: it discards the diversity of reasoning paths that could be combined to produce stronger answers. wastes much of the compute spent generating samples and risks reward hacking (Skalse) et al. 2022b a Jchihara et al. 2025): : the candidate that maximizes judge's 's score is not always the
most correct or useful.
In today's 's fast-shifting LLM landscape, where leaderboard wins change hands quickly, treating quality as a single monolithic dimension is increasingly outdated. In practice, there rarely a single "best" answer, diverse outputs often complement one another. This motivates our central question: can we go beyond selection and design a method that makes fuller use of all generated samples? We propose FUSION, simple synthesis-breg based alternative to BoN that exploits the generative abilities of LLMs to integrate complementary signals across candidates- -triltyy--rulyyyieei making, rather than merely taking, the best of N.

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Published as a conference paper at ICLR 2026
Generations
Fusion of N
Prompt
Result
FUSION principle: Multiple generations (here N 4, from multiple models) get fused into one final generation combining the strengths of each individual generation.
Figure I:
We treat aggregation as synthesis problem rather than selection problem. Figure Iillustrates our idea: We use strong LLM judge, the fusor, to integrate the complementary strengths of multiple candidates into single answer. Our proposed Fusion-of-N (FUSION) method is simple, general, versatile and can directly replace BoN with no modifications beyond access to a reasonably strong generative LLM that acts as a fusor. The polylithic understanding of quality allows to decom- pose complex problems into compositional ones that are more tractable. FUSION optimizes across samples and integrates complementary insights into a single, higher-quality answer. Going beyond the initial sample pool is especially valuable when the pool is strong and diverse, and for problems that naturally benefit from diversity. Intuitively, this mirrors how experts synthesize knowledge from multiple domains and perspectives.
We perform comprehensive evaluation of FUSION a replacement for BoN across test-time scaling and data generation: For test-time scaling, we measure the effectiveness of FUSION with multiple samples from 8B and 111B models on open-ended generation and machine translation tasks. We evaluate the impact of synthetic data generated with FUSION in terms of data quality and downstream results after fine-tuning a and 111B model on open-ended prompts, math and factual reasoning tasks. In both setups our evaluations are spanning multiple languages to test FUSION under diverse and challenging conditions.
Our results show that synthesis is not only more effective, but also more sample-effcciet: FUSION consistently outperforms BON under the same sampling budget, and some cases even surpasses the oracle, revealing that selection is not the upper bound. It proves robust under weaker teacher pools, showing that diversity can be leveraged even when individual contributors are limited. We observe that fine-tuning FUSION data enables models to outperform even the strongest single teacher, showing that synthesis distills collective knowledge in ways that selection cannot. Fi- nally, analysis provides the first detailed look into the mechanisms of synthesis, uncovering both its strengths-sampl efficiency, robustness, and adaptability and its limits on tightly constrained math tasks. To summarize, our contributions are:
Conceptual shift from selection to synthesis. We present compelling evidence for reframing ag- gregation synthesis problem. In contrast to previous works in this direction (section 6 FUSION is simple, easily customizable and works
box. making it an attractive substitute for BoN.
Demonstrated gains across test-time scaling and data generation. FUSION consistently outper- forms BoN in both settings where candidate aggregation is used today: (i) test-time scaling, where yields substantial improvements (e.g.. +3.8% win-rate GEMINI2.5-PRC on mArenaHard-v2, +3.7 XCOMETXL on translation), and (ii) synthetic data generation, where it produces higher- quality datasets that drive downstream gains across diverse tasks (+2.5% on mArenaHard- vs GEMINI2.5- FLASSHH +0.8 on WMT, +1.0% on GeoFactX answer accuracy and +0.8% reasoning quality).
Robustness and efficiency across models and settings. Our analysis shows that FUSION is more sample-efficient and robust than BoN, It maintains high performance with smaller or weaker teacher pools, benefits from larger fusor models, and scales effectively with added test-time compute. These properties make a practical and generalizable approach for both test-time scaling and synthetic data generation, even under constrained imperfect conditions.
This work redefines how we measure and leverage LLM outputs. Instead of treating generations isolated candidates, embrace their diversity and complementary strengths, synthesizing them
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Published as a conference paper at ICLR 2026
into more powerful, coherent results. Our findings show that treating LLMs collaborators and not competitors unlocks higher-quality outputs and more impact on downstream usecases, pointing toward a fundamentally more effective paradigm for large-scale language model deployment.
2 METHODOLOGY: FROM SELECTION TO SYNTHESIS
Selection with Best-of-N (BoN) Given prompt H, a pool of candidates y E Y, and scoring function S, the BoN method selects the optimal candidate y" by maximizing a scalar score: y" arg myy S(y,x).
The scoring function could be a specialized reward model as used in rejection sampling for synthetic
data generation (Grattafiori et al. 2024), or test-time scaling (Cobbe et al.) 2021). The score could also be produced by a generative LLM that is prompted to predict a scalar score (Kim et al. 2024), though in practice trained classifiers often perform better, for instance many top models on Reward- Bench (Malik et al.) 2025) leaderboard are sequence classifiers. These type of scoring functions typically optimized on verifiable domains and pairwise human preferences (Cobbe et al. 2021
Ouyang et al. 2022).
Limitations of BON The limiting factors for selection with BoN (1) the alignment with the desired task (Lambert et al. 2020 Pan et al. 2022 Ichihara et al. 2025 Viswanathan et al. 2025), (2) and the quality of the generated sample pool (as per definition, the final generation can only be as good as the best of the candidates). For domains with verifiable problems, the aligmment can easily be improved by scaling up training data for the reward model (Liu et al., 2025). but even with expensive ensembles (Eisenstein et al., 2024) risks of overfitting to an imperfect proxy re- main (Stroebl et al., (024).. Scaling up reward model alignment is less transferrable to open domains like chat or open-ended question answering, where this signal needs to be obtained from human feedback (Huang et al. 2025 Viswanathan et al., 2025). Similarly, a poor initial sample pool can be improved by diversification (Chen et (2025) or optimized sampling (Khairi et al. (202), simply scaling up the number of samples, sometimes requiring thousands of samples for test-time scaling to be effective (Stroebl al. 2024 Brown et al. 2024), which makes it extremely resource-intensive.
Synthesis with Fusion-of-N (FUSIoN) A fusor model F' (a standard LLM) generates a new re- sponse y² based on the input prompt X, and a pool of candidates Y: y² F),,Y), y² $
This means that the final generation y" is conditionally dependent on the other candidates, and, can-in contrast to BoN-exceed the original pool in quality (see Section (5.. It can be seen as a form of collaborative refinement: Rather than only selecting a sample according to a monolithic notion of quality, FUSION goes beyond and productively integrates a polylithic notion of quality into the synthesis of better sample. The polylithic view, meaning that we acknowledge the existence of higher and lower-quality parts in each sample, particularly well suited for long generations for complex prompts. FUSION can "mix and match" fragments of variable size (e.g. tokens, terms, sentences, ...) that stand out in quality in each of the provided samples (see the example in fig. [5]. BoN is captured special case: the fusor still has the option to copy one whole generation if outperforms all others for the entire sequence.
Components of FUSION
The success of FUSION depends the capabilities of the judge to comparatively evaluate, extract and aggregate the best parts of each generation. We will show in section 5 that there appears to be a threshold in model size that needs to be crossed for FUSION to work without any specialized training. Our analysis also shows that the choice of fusor, given certain model size, seems less important than the composition of the sample pool. One major advantage over using a reward model, is that the FUSION prompt (ours in table 4 allows for in- context learning and adaptation without any training. It can be tuned to steer FUSION behavior in ambiguous cases, such as concerning safety standards (e.g. with constitution (Bai et al.) (022)) tone or model identity, and how much it should attempt to integrate parts from all samples or also discard the worst ones entirely. With chain-of-thouge models as fusors, we also have the possibility to scale up FUSION compute where desired. In preliminary experiments we found it important to instruct the model to not only focus on the best, but also discard the worst parts. We have not conducted any prompt tuning beyond that, but practitioners are invited to tune their FUSION prompt their use cases.
prompting (Wei et al.) 2022)
reasoning
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Published as a conference paper at ICLR 2026
3 EXPERIMENTAL SETUP
Our experiments span two prominent environments for BON, the first focused on test-time scaling, and the second focused on synthetic data generation. In both cases, our intervention of replacing BoN by FUSION is minimal: Both methods receive the identical set of generations for the same prompts, but aggregate it differently to produce the final generation.
3.1 MODELS FOR TEST- TIME SCALING
We study the test-time scaling behavior for multilingual models of two sizes: AYA EXPANSE 8B and COMMAND at 11IB. We use temperature sampling at T = 0.7 to generate N = samples from each model (see Figure for various N). We use competitive in-house multilingual Reward Model (RM) for scoring the candidates in BON and COMMAND A as fusor in FUSION (ablation and comparison to GEMMA models (Team et al. 2025a) in fig. 5p.
3.2 MODELS AND DATA FOR SYNTHETIC DATA GENERATION
Models. For synthetic data generation, we employ five open and strong models of varying size and families as teachers: DEMMA3-27B--
QWEN3-235B, DEEPSEEK- V3 and COMMAND A [Team et al., 2025a b Yang et al. 2025 DeepSeek AI et al. 2025 Cohere et al. 2025). We sample a low temperature completion (T
HIMI-K2IINSRRUS
0.3) from each of them to generate
the pool of samples for each prompt. From this pool, we then select one completion for supervised fine-tuning (SFT), either with RM COMMAND A fusor. Ablations regarding pool composition For fine-tuning, we choose an 11IB iustruction-tunel LLM our baseline model for our main SFT experiments, and perform an ablation with smaller 7B Base LLM baseline (Appendix G). Finetuning hyperparameters are listed in appendix B We do not apply test-time scaling on top of our fine-tuned models.
and fusor model choice will follow in table 2
General Fine-tuning Dataset. For our main fine-tuning experiments, we randomly sample 10k prompts from UltraFeedback Binarized (UFB) (Tunstall al., (023), an English preference dataset with 61k pairs that was previously used to measure the impacts of data composition in fine- tuning (Odumakinde et al., 2025] Li et al.) (0256).. We translate the prompts automatically into 9 languages: German, French, Spanish, Chinese, Japanese, Arabic, Korean, Italian, Portuguese.
Reasoning Fine-tuning Dataset. Learning to reason is often approached through synthetic data, where models imitate reasoning traces from single teacher (Shridhar et al. 2023 Muennighoff al. 2025 Hwang et al., 2025). Here, we apply our FUSION approach to learn to reason from mul- tiple teachers. We add second, smaller, batch of prompts for domain-specific reasoning tasks: We add the prompts from the GeoFactX dataset (train split) for geography-based factual reasoning, and translated s1k prompts (Hwang al. (0255) for mathematical reasoning. The prompts machine- translated from English and cover five and ten languages, respectively. We prompt the teachers to generate mhains-of-thoughe and answers for training student model (details appendix C).
3.3 EVALUATION BENCIMARKS
We focus on challenging, multilingual benchmarks that test our models' generative abilities and cover tasks of three domains (full details in appendix Dr:
Open-ended challenging prompts (Arena) are sourced from marenaHard V.2 (Khairi et al. 2025) (11 languages). Quality of generations is measured in terms of win rates as determined by an LLM judge (gpt-4c f 200-2-22---551 (1) in direct comparison to the commercial GEMINI2.5-FLASH and GEMINI2.5 --PR models and (2) in head-to-head comparisons of FUSION vs BON.
Machine Translation (WMT) prompts are sourced from WM724++ DDettchh et al. 2025 Koemi et al.) 2024a) (English 10 languages). Quality of generations is measured with XCOMETXL GGerrrero et al. 2024), a state-of-the-an multilingual translation evaluation metric.
IIt scores an average score of 76.1 on the English RewardBench2 (Malik et al. 2025 which at the time of submission (24 Sept 2025), places it at 1 11th place. On multilingual RewardBench (Gureja al. 2025) it scores an average of 87.6 across languages, beating all openly benchmarked models.
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Published as a conference paper at ICLR 2026
50
+9.6
+9.5
2.44
3..5
-1.5
Win Rate
40
+3.2
30
+9.0
+10.8
+7.6
+8.00
+2.1
+9.5
20
+4.0
English
German
French
Spanish
Russian
Japanese
Chinese
Arabic
Portuguese
Italian
Korean
FusioN
BoN
CommendA-1118
Aye-88
Figure 2: Test-time scaling with N = FUSION raises win rates against GEMINI2.5 Arena across languages. It largely outperforms BoN with the same set of samples, for both AYA EXPANSE 8B and COMMAND A models. Gray markers indicate greedy baseline performance.
Reasoning evaluations target the reasoning fine-tuning mix and include the GeoFactX test split (Hwang al. 2025) (5 languages) and math problems from MGSM (11 languages inel. En- glish) (Shi et al. 2022). Both are evaluated terms of accuracy the final answers, and we additionally inspect reasoning quality for GeoFactX, following (Hwang al. 2025)
RESULTS
4.1 TEST-TIME SCALING
FUSION brings substantial improvements multilingual open-ended generation tasks. We evaluate both COMMAND A and AYA EXPANSE 8B Arena when scaling test-time compute (Sec- tion 3.1 and comparing gains from using FUSION vs BON. The results in Figure significant gains win-rate against GEMPNI2.5-PRO across both languages and models (detailed breakdown in Tables 1 and 14] For AYA EXPANSE 8B we see impressive jumps in win-rate of up to +10.8% in French. Similarly. FUSION outperforms BON for COMMAND A out of 11 languages. Sur- prisingly, in cases like German (+9.5%) and Spanish (+7.8%) the gains from using FUSION on only samples allow COMMAND to over GEMINI2.5- 15M (absolute win-rate > 50%), the top model in Arena. This special case, where fusor and sampling model are identical, FUSION can be seen as a form of very effective self-refinement (Ranaldi & Freitas 2024). The gains from FUSION are also consistent at different scales (number of samples), tasks and in direct comparison which we investigate deeper in Section and Appendix
Oracle
Fusioh
+5.0
X-ComebLL
+3.5
+3.0
+9.5
+16.3
+3.6
+7.0
+114
German
French
Spanish
Russian
Japanese
Chinese
Arabic
Portuguese
Italian
Korean
Figure 3: FUSION BoN ORACLE (the highest scoring sample according the ground truth) in Translation, eтror bar show std-err. Bars with bold border (German, Russian and Chinese) are cases where FUSION outperforming the ORACLE.
Synthesis beats selection in machine translation. When testing on WMT we can the refer- ence translation to score each candidate generation against with the task metric XCOMETXL. We thus identify the "oracle" among our samples, and compare its quality to the quality of samples selected by BoN with its (imperfect) RM, or the sample synthesized by FUSION. Figure 3 shows the comparison for N generations from COMMAND A sampled temperature 0.7 for the

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Published as a conference paper at ICLR 2026
ar
de
fr
it
ja
ko
pt
zh
Avg
WMT Arena
BoN
43.9
43.1
42.7
43.3
44.5
44.2
43.6
45.1
43.4
43.7
44.8
433.8
FUSION
45.1
44.3
48.0
46.2
48.3
48.4
43.8
48.4
45.0
45.2
46.3
46.3
+1.2
+1.2
+5.3
+2.9
+3.8
+4,2
+0.2
+3.3
+1.6
+1.5
+1.5
+2.5
BoN
73.8
90.9
86.4
83.5
85.6
81.6
81.7
85.1
83.0
78.6
83.0
FUSION
74,6
91.2
87.2
84.3
86.2
83.1
82.8
85,5
83.5
79.8
83.8
A
+0.8*
+0.3*
+0.8
+0.8*
+0.6
+1.5"
+1.1
+0.4
+0.5
+1.2*
+0.8
Table 1: Downstream evaluation of BON/FusION-fne-tune 111B models on Arena (% win rate against GEMINI2.5FFLASII and WMT (XCOMETXL, en- -): FUSION outperforms BoN consistently across tasks and languages. * indicates significance for WMT results according to comet-compare (paired t-test and bootstrap resampling (Koehn) (004)). The baseline starts with an average score of 22.8 22.8% for Arena, and 82.0% for WMT.
WMT24++ test set. FUSION outperforms BON with large margins across languages, reaching dif- ferences of +1 +1..4 in Korean. More importantly, FUSION outperforms the ORACLE selection in the Genman, Russian and Chinese translation with gains of +0.8 in the latter, meaningful improvement in terms of XCOMETXL scores. This confirms the utility of our proposed synthesis framework of aggregation. Instead of treating generations as competitors in a zero-sum game, we should treat them as collaborators whose strengths can be integrated.
4.2 SYNTHETIC DATA GENERATION
FUSION yields consistent multilingual gains with downstream impact. We compare gener- ation and translation quality of the model fine-tuned on FusioN-generated data with the model trained on BoN NoN-ggnerated data in table (see Appendix G for 7B results). All hyperparameters, prompts and teacher outputs are identical for both variants. Given that we only change the way we aggregate the samples, we find surprisingly notable and consistent improvements of FUSION over BON, across languages and two tasks. On average, model fine-tuned on fused generations yields XCOMETXL scores +0.8 higher on WMT24++, delta that can be expected to represent human preferences with around 73.6% accuracy, according to estimates in (Kocmi et al. 2024b Similarly, FUSION improves win-rates against GEMINI2.5 5--NAAS by +2.5% over BoN. With only minimal intervention in the data generation phase, the results reveal a remarkable downstream im- pact, underscoring the powerful ripple effect that even modest improvements in data generation achieve. The 7B model finetuned with FUSION outperforms the one finetuned with BoN on WMT, but Arena as we discuss in Appendix G
FUSION leads to better multilingual factual rea- soning [ how the model fine- tuned FUSION outputs outperforms the model fine-tuned on BoN in terms answer correctness and reasoning score across four out of five lan- guages, with minor regression in Hindi. The fine- tuned models do not only outperform base model (by +8.1% in answer correctness on average for BoN, also the fusor model (by +9.1% for FUSION),
70
Answer Conectrees
60
50
30
3.4% and 4.4%, respectively, see full results in ta- ble 10). This validates our hypothesis that we can effectively leverage the wisdom of the crowd with- out being bounded by the model that performs the fusion (see also Appendix G). It is worth noting that this holds even for the languages that the fusor model (COMMAND A) officially does support (Swahili and Thai). On MGSM, however, we found some cases where FUSION scores below BoN, which we discuss in appendix E.2
Reasoning Score
ja
RusioN
x
BoN
G).
Figure 4: Downstream evaluation mul- tilingual factual reasoning on the Geo- FactX test set. FUSION outperforms BoN notably in both reasoning quality and answer correctness in 4/5 languages.
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Published as a conference paper at ICLR 2026
5 ANALYSIS
Our results reveal consistent improvements across setups and languages with out-of-the box fusor and a small set of samples. To find out, where and how FUSION working, we conduct a range of ablations, diving deeper into specific sub- suus-quesiions.
What makes the fusor work? In Figure 5 we ap- proach this question from two angles: (i) the scale of the fusor (number of parameters), and (ii) how the fusor model is utilized. We evaluate the size effect by varying the fusor from the 4B Gemma-3 to the 111B COMMAND A measuring the resulting average win-rate test-time scaling on Arena. We find that for FUSION (blue) larger scale is needed for the fusor to work out of the box. Importantly, FUSION
50
40
27B
111B
Win Rate
continuously benefits from increasing the scale of the fusor as we see an increase in win-rates of +5.5% as we go from the 27B fusor we use the same fusor models as rater in BoN (red) (prompt in Appendix A), smaller models fare better, but these gains vanish at scale, which aligns with the observation that even the strongest genera- tive models such GEMINI2.5 5 5-PRO are still outper- formed by classifier RMs on classic reward scoring benchmarks (Malik et al. 2025). Overall, FUSION utilizes the judge capabilities at larger scale more ef- fectively than BoN. Smaller fusors likely need spe-
RusioN
BoN
I1IB fusor. When
10a
Fusor Size scale)
Figure 5: Size of the fusor matters: Small LLMs might serve well as scalar judges in BON, but generative fusion capabilities get unlocked at larger scale, here measured in win-rates on Arena, averaged across lan- guages, shaded areas represent std-err.
cialized training for FUSION, which related work has done for math 2025).
Qi et al.
2025
Zhao et al.
Which method is more sample-efficient? We compare the sample efficiency of BoN and FU- SION under the same test-time scaling budget. In Figure 0 measure win-rates four languages (See appendix G for language break- down). We observe that FUSION is more efficient at the lower scales (N < 10), improving win-rate against GEMINI2.5-PRO by +6% with only = 2, where BoN needs twice more samples to achieve similar gains. Gains for both methods plateau be- yond N = 7, but FUSION consistently makes fuller use of each generated sample, making FUSION the more efficient choice for low-budget scaling. Note that BON requires independent samples, which parallelizable, while FUSION encodes all sam- plcs together (more details in Appendix Db. Despite this, FUSION shines at small N, making every to- ken count and turning even a few samples into high- quality, integrated solutions. With efficient long-context implementation, it achieve strong scaling performance while fully leveraging the diversity in the sample pool.
Arena across
Win Rate
35
FusioN
BoN
2
3
4
6
8
10
11
12
13
14
15
Scale (N)
Figure 6: Scaling test-time budget: Win- rates are shown against GEMINI2.5-PRe on 4 languages from marenaHard- V2.00 Shaded areas are average std-err across lan- guages.
How is synthetic data quality affected by the fusor and teacher pool? The quality of the syn- thetic data generated is dependent on the quality of the pool of the samples and the fusor used. We measure quality of the data averaged across 10 languages using win-rates against GEMINI2.5- FLASH for 1k examples fusor affect data quality. Across all modifications of higher quality compared to the BoN data with +4.4% in the default setup with all five teach- Even when perform FUSION with on this benchmark DEEPSERK-VE (based
UFB, and report in table 2 how modifications to the teacher pool and
that F synthetic data is

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Published as a conference paper at ICLR 2026
# Candidate Pool
Method
WR
100
0
CMD: Sample
57.9
12.1%
10.6%
3.8%
1
FUSION
65.4
80
15.
874
2
All Teachers
BoN
61.0
Percentage
3
Fusor=DS
63.9
60
13.9%
24.0%
16.
4
FUSION
65.0
Weaker Pool
40
5
BoN
60.9
19:7%5
6
FUSION
62.9
Smaller Pool
7
BON
60.2
15.0%
15.0%
8
FUSION
59.0
English
Swahill
9
DS: 5 Samples
BoN 58.9
DeepSeek-VV
CommansiA
Owen3
Gemma'
Kimi-K2 Multiple
Unmatched
Table 2: Ablation pool size and di- versity: win-rates (WR) vs. GEMINI2.5- FLASII using 1k random UFB sam- ples, averaged over 10 languages. DS: DEEPSEEK- V3, CMD: CommandA.
Figure 7: Diverse teacher contributions: Anal- ysis of teacher contributions to the final output of FUSION subset of GeoFactX (50 samples per language) across unsupported languages.
100
122
16.4%
160%
Unmatched
23%
23.3%
Kim-K2
40
67
Ower3
442%
48.2%
42.5%
DuspSook
Arabi
Erglshh PusioN Contribution
Chirese
Arabic
End
Chinasc
BoN Selection
Figure 8: Contributions to the final generation: Analysis of different teachers in the pool con- tributing the final output of FUSION or BoN. For the multi-teacher we look at FUSION outputs on subset of UFB (50 samples per language) with 5-teachers pool.
on reward scores from our internal RM) (43), we see only a small drop in quality while still out- performing BoN. When replace GEMMA3- -----B-BITT with GEMMA3-4B-IT in the teacher pool (weaker teacher pool) (#4+#5), both methods are minimally of only four teachers (without KIMI-K2-INSTRUCH #6+#7) affects FUSION proportionally more, but still wins over BON. If we sample only from a single teacher (here DEEPSEEK- V3, #8+#9) win-rates drop substantially, highlighting the importance of diversity in the teacher pool. Overall, these ablations show that FUSION is more robust under weaker ensembles than BoN.
using a smaller pool
How does FUSION balance its pool? We track the sources of contribution in FUSION by surface- level sequence matching in Figure 8 details and more bias probes in appendix F with an example in fig. [5). We see that a large proportion of the FUSION output directly taken from the teachers' outputs, forming coherent synthesis. Only small fraction of words is unmatched, where the fusor adds "glue" or reformulates teacher outputs. While both methods show similar high-level preferences (favoring COMMAND A the most and GEMMA3-27BB. even the less preferred ones. Finally, we inspect the contributions the GeoFactX data, because contains languages not officially covered by the fusor (COMMAND A). Figure shows that FUSION remains robust, with its preferences shifting to utilize GEMMA3-27B-I IT the most.
the least), FUSION integrates
we compare the outputs of FUSION, BoN, and all teachers on various metrics on the GeoFactX train set. For answer correctness, FU- SION achieves the highest accuracy with 58.6%, despite the fusor (COMMAND A) scoring the low- est. FUSION also obtains the highest reward score, from our internal RM, the GeoFactX mix- the very metric BoN is optimizing. We note that FUSION has low 98.9% language correctness, likely due our fusion prompt being English-only (Appendix A), but leave studying these effects
the fusor putting a ceiling on the quality? Table 3
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Reward Score
Answer Correctness
Language Correctness
Model
FUSION
7.86
58,56
98.86
BoN
7.11
49.77
99.05
CommandA
6.30
40.52
99,61
DeepSeck
6.43
48.89
98.99
Qwen-3
6.46
41.06
99.58
Gemma-3
6.52
41.89
92.11
Kimi-K2
6.64
46.62
99.56
Table 3: Data analysis for teachers and aggregation outputs on GeoFactX across languages.
to future work. These results show that FUSION is not limited by the fusor and in fact more dependent on the sample pool.
Opportunities for strengthening FUSION. During test-time scaling we find that FUSION ben- efits English more than other languages. is likely that the skills that are required perform successful FUSION are not evenly present in all languages. Although we do find gains of FUSION over BoN unsupported languages of the fusor for GeoFactX, BoN might be the safer choice for cross-lingual transfer to lower-resource languages (Hong al. 2025), while generative model capabilities are still lacking behind. We also found more mixed results when testing on MGSM (appendix E.2 which might indicate that close-ended tasks either just not well suited to be addressed by generative ensembling, or that the fusor would need specialized training for such a specialized domain that RMs usually well trained
RELATED WORK
The principle of learning from ensembles has led advances in many areas of machine learning, and can be integrated into LLMs in various forms (Wang et al. 2023b Lee et al. 2023 Huang et al., 2024 Wan et al., 2024). In this work, we focus on integrative output ensembling. This approach can be seen as an instance of Mixture-of-Agents (MoA) (Wang et al., 2024), framework where multiple agents organized in layers iteratively enhance the output. Our approach stands out through simplicity: We show that FUSTON becomes effective already in single aggregation step with single fusor, even in diverse and challenging setups, thereby constituting an attractive alternative for BON, which is thanks to its simplicity- much more widely adopted framework than MoA.
LLM-Blender (Jiang et al. 2023) follows a similar idea, but requires two separate modules, one for pairwise ranking, and one for fusing top-ranking outputs. It operates on the basis of pairwise com- parisons (which require training a specialized model), while we pass all oulputs at once to the fusor, so that it can evaluate them in context. Other contemporaneous related works also require training such specialized aggregator modules (Qi et al. 2025 Zhao et al., 2025 Li et al. (025b), while our approach is effective without any training. Thesse works focus primarily
reinforcement learning or reasoning for verifiable tasks like math and code. For such specific scenarios with available ex- pert raters, [i et al. (2025a) warn that MoA might
be sufficiently robust lower-quality inputs. For our diverse open-ended evaluations, however, we find FUSION fairly robust to the teacher pool, and sampling from single teacher-the proposed solution by Li et al. (2225)) 2025a)-perfore ppefoorm signifi- cantly worse. Jayalath et al. (2025) find that fused Lingl--teccerr roll-outs can nevertheless provide valuable supervision in RL training, even without any fusor training.
Our approach can also be cast as combination of parallel and sequential test-time-scaling Welleck jtt al., 2024, Snell al. 2025), with N parallel steps and one refinement step. Balancing these options can be seen as search problem (Inoue et al.) (222)). This poses an interesting avenue for future work, where FUSION operates with adaptive compute (rather than fixed N+1) customized for each input. This flexibility might be needed for mimicking human cognitive processes more closely (Zhang et al. 2024). Overall, our work complements very recent advancements discovering collaborative synthesis inference, enhancing understanding of its benefits and limitations. Even in its simplest form, our approach demonstrates gains across diverse applications, including test-time scaling and model supervision.

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Published as a conference paper at ICLR 2026
7 CONCLUSION
Our work thoroughly investigates and challenges the to-date standard practice of BoN in test-time scaling and synthetic data generation. Our experiments strongly support replacing it by FUSION in these scenarios to make most of the costs that are already incurred from generating and eval- uating multiple samples. Across range of challenging multilingual tasks, FUSION consistently outperforms traditional winner-takess greater sample efficiency, and stronger downstream performance. Importantly, FUSION leverages the strengths of multiple models, even when some are weaker, showing robustness and adaptability. These results highlight a shift in how we should think about evaluating and utilizzig LLM gener- ations: rather than measuring quality monolithically, embracing their polylithic nature allows us to integrate diverse strengths, unlock latent potential, and achieve improvements that were previ- ously inaccessible through selection alone. FUSION points toward more effective and sustainable paradigm for leveraging the collective capabilities of today's leading LLMs.
approaches like BoN, delivering higher-qualige outputs,
ACKNOWLEDGMENTS
First, we thank our colleagues for their help in various stages of this project: Wei-Yin Ko, Kylie He and David Mora for the help with post-training, Kelly Marchisio for her advice regarding bench- marking, Thomas Euyang for the beautiful illustration, Madeline Smith for the help with communi- cations, Sara Hooker and Ye Shen for feedback in discussions in early stages of the project, and the remaining Cohere Labs for their helpful feedback throughout all iterations of this project. Further- more, we would like to thank Jaedong Hwang for sharing data and evaluation code for the synthetic reasoning experiments. Last but not least, we would like to thank the anonymous human and/or AI reviewers that have helped us improve the robustness of our evaluations.
ETHICS STATEMENT
Training on synthetic data comes with inherent risks of propagating and amplifying biases (Ahn) et al. 2022; Shimabucoro et al., 2024] Mohammadshahi & loannou 2025). We hope that by in- creasing diversity in the teacher pool, we can reduce model-specific biases to propagate (as opposed to learning from one teacher only), and prevent loss of diversity in the generated data (Briesch al. 2024).
Regardless, cannot strictly protect the student model from adversarial teachers, probably less so with FUSION than BoN because they might be more prone to prompt injections. Our tests revealed robustness with respect to the quality of the teacher pool (section (), but have not tested truly adversarial inputs. We rely on the user to verify teacher suitability and potentially add any sanity checks. In contrast to BoN, the FUSION framework allows for flexible instructions that could include c.g., constitution (Bai et al. 2022) or specific safety guidelines. In practice, FusION could also be prepended with a hard filter for unsafe or lowest-quality samples (e.g. language compliance via language identification), so that the undesired information does not even get to the aggregation stage.
We also perform additional analyses for typical LLM judge biases in appendix F and find no ev- idence for self-preference, but slight position bias, i.e. the fusor preferring samples that it presented first more than those that come later.
We would also like emphasize that any use of such ensembling needs to respect all terms of use and licenses of the individual teachers, which lies in the responsibility of the user.
REPRODUCIBILITY STATEMENT
Fusor and teacher models that we use in this work are publicly available (section 3 well as the prompts for -uningn We transparently report prompts and instruction templates for LLM evaluation (appendix A). and benchmark metric implementations (section 3 Where models not public (student model in the experiments on synthetic data generation, and reward model), we re- port scores public benchmarks that allow anchor our experiments. The data generation pipeline
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that we describe in detail in appendix C is not perfectly reproducible due to inherent randomness in the sampling process. Therefore, we release synthetic data for BoN and FUSION where licenses allow!! In addition, we follow the recommended practice for generative multilingual LLM evalua- tions Kreutzer et al. 2025) and release our pairwise evaluations that rely on LLM judge:
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Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro Von Werra, Clémentine Fourrier, Nathan Habib, et al. Zephyr: Direct distillation of Im alignment. arXiv preprint arXiv:2310.16944 2023.
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Based on the provided Instruction and Generated Texts in language, fuse them into better generation that combines the strength of each of them. Do so in two steps: First, compare the Generated Text with a focus on what sets them apart in terms of content, language quality and responsibility, highlighting strengths and weaknesses. Second, fuse them into a new final generation that combines the best aspects of each of them while avoiding the weaknesses.
The fused generation should be adequately responding to the instruction, sound nat- ural native speaker, and be focused conveying the most relevant and accurate information in responsible and ethical way.
Output Format
Comparison: (short explanation of the strengths and weaknesses of each generation) Answer: The final fused generation ]]
Context
Instruction
prompt
Generated Texts
generations Please analyse the Generated Texts, discarding any unsafe or unethical generations and provide your fused text. Remember stick to the requested Output Format, providing first a short explanation and then putting the final fused generation inside double brackets [[]].
Table 4: Prompt used for FUSION, including placeholders Generations are randomly shuffled and enumerated, presented one per line.
A PROMPTS
A.1 FUSION
We provide the prompt used by the fusor in Table 4
We use the same prompt across all tasks, setups. fusors and languages. Table[]]shwws the prompt for using the fusor model scalar rater. We also provide the English Version of the instruction prompts used in our evaluation in Table
B FINE-TUNING HYPERPARAMETERS
We train the 111B baseline on the synthetic data generated from our UFB mix with batch size of 16, cosine decay with peak learning rate of 5e-6 using Adam optimizer across 64 Nvidia H100 GPUs for 250 steps. For the extended mix (UFB and Math+GeoFactX) we use the same hyperparameter with increased number of steps of 323. We train the 7B models on the UFB mix with 16 GPUs with the same parameters.
C SYNTHETIC REASONING DATA
Hwang et al. (2025) build two datasets for improving multilingual reasoning abilities: sik-X for multilingual mathematical reasoning and GeoFactX for geography-based multilingual factual rea- soning. The multilinguality stems from automatic translation of the prompts, of the slk dataset from (Muennighoff al. 2025). and of synthetically created English prompts that designed variety of regions. For sIk-X the reasoning traces and answers from Qwen-2. 2. 5- 5-Inst ruct 72B s1k are also translated (via the Google Translate API). This has some undesired side effects where the mathematical notation or the answer formatting gets corrupted, e.g. with white spaces around ВТTX math symbols. For both datasets, we only work with the translated prompts, and use our pool of teachers to generate multilingual responses. For analysis and for evaluation, verified answers provided in the GeoFactX dataset as ground truth for the evaluation of accurateness of answers. For slk-X, correctness analysis of the data is hindered by the inconsistencies in format for the (translated) silver answers of Qwen, which makes the extraction of answers non non-trivial. We leave this analysis for future work.
cover
use the human-
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Please act as fair judge. Based on the provided Instruction and Generated Text, analyse the Generated Text and provide 1-5 integer score. The given instruction is in language and the response should also be in language Your selection should be based on your judgment well the following guidelines for each possible score:
1. The Generated Text is unintelligibly written (incomplete sentences, leaps in logic, flagrant mechanical errors) has majorly incorrect or unverifiable information.
2. The Generated Text is occasionally difficult to understand, dotted with minor fac- tual mechanical errors, or missing crucial formatting elements.
3. The Generated Text expresses useful information, is readable, has no factual errors, and has no more than minor mechanical error or two. Though may be informative to those unfamiliar with the subject matter, it is not overly insightful, engaging, or likely to hold up to expert scrutiny.
4. The Generated Text clearly expresses useful information an expert level, is read- able, and has no factual or mechanical errors. It could just use quick adjustment with tone or length.
5. The Generated Text clearly expresses useful information at an expert level, is read- able, has no factual or mechanical errors, and is the perfect length and tone with regard
to the prompt.
Output Format
Analysis: xXX Answer: [[ SCORE ]] (this should be an integer from 1-5 and nothing else; the score should be enclosed in double brackets indicated)
Evaluation Information
Instruction
message
Generated Text
generation
Please analyse the Generated Text and provide a 1-5 integer score according to the guidelines. Remember to stick to the requested Output Format, providing analysis and putting your final score (an INTEGER in 1-5) inside double brackets (0].
Table 5; Prompt used for BoN with generative models, including placeholders
Task
Prompt
MGSM (en)
Solve this math problem. Give the reasoning steps before giving the final answer on the last line by itself in the format of " "An- swer:". Do not add anything other than the integer answer after "Answer":
WMT24++
You are professional src.lang tgt.lang translator, tasked with providing translations suitable for use in tgt_land Your goal is to accurately convey the mean- ing and nuances of the original sra_lang text while adher- ing ttgtllang grammar, vocabulary, and cultural sensitivi- ties. Produce only the Egt_lang translation, without any addi- tional explanations or commentary. Please translate the follow- ing sre lang text into tgt lang countty)) source.texx
Table 6: Instruction prompts used for evaluation, including task-specific p1 accholders MGSM prompts are taken from the simple-evals] library, we only list the English one here but use them in the respective target languages.
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For evaluation of fine-tuned models the test split GeoFactX, follow the procedure in (Hwang et al. 2025). We prompt a LLM judge (here GPT-4o, deviating from (Hwang et al, 2025) which uses Qwen,
wanted avoid self-bias) to score the reasoning traces for quality. We compare the final answer in the generation against the list of the correct answers provided the task following the implementation by |Wwag et (2025), and also verify the language of the response according to their implementation
D EVALUATION
We describe our set of evaluation benchmarks in more detail.
mAremaHard V.2 (short: Arena) This data contains 498 translated challenging prompts from mArenaHard-V22 V2.0 across 23 languages (Khairi et al. 2025). Quality of generations is measured in terms of win rates direct comparison to the commercial PRomodels in addition to head-to-head comparison of FUSION vs BoN. We mainly compare against GEMINI2.5-PRe in the test-time scaling environment where we use production ready LLMs with extra compute. In the synthetic data generation environment, we benchmark weaker LLMs fine-tuned on a small synthetic dataset, hence we switch to GEMIN12.5-FLAS parison is done by LLM judge, here GPT-4o. We focus subset of 11 languages: English (en). German (de), French (fr), Spanish (es), Russian (ru), Japanese (ja), Chinese (zh), Arabic (ar), Korean (ko), Portuguese (pt), and Italian (it).
GEMINI2.5-
The pairwise com-
WMT24++ (short: WMT) This dataset contains translation problems sourced from the WMT 2024 machine translation shared task (Koemi al. 2024a) expanded to more languages (Deutsch) et al. 2025). Quality of generations is measured with XCOMET-XL 8 state-of-the-art multilingual
translation evaluation metric (Guerreiro al. 2024). We the prompt in Appendix A and we focus on translating from English to the following languages: Arabic (ar), German (de), Spanish (es), French (fr), Italian (it). Japanese (ja). Korean (ko), Portuguese (pt). Russian (ru), Chinese (zh).
MGSM:
This benchmark contains 250 mathematical problems at grade-school level in 11 lan- guages (bn, de, en, es, fr. ja, ru, sw, te, th, zh), originally translated from English (Shi et al. We prompt models to think step by step before outputting the final answer, following the S simple-evals implementation The evaluation metric the accuracy of the final answer.
2022).
GeoFactX We follow the prompting and evaluation process recommended by Hwang et al. (2025) and evaluate reasoning traces and final answers with an LLM judge and against gold answers, re- spectively (details in appendix
E ADDITIONAL RESULTS
E. TEST-TIME SCALING FOR MATH
We include additional analysis of FUSION on mathematical reasoning. On the MGSM benchmark (250 prompts per language), FUSION performs on par with BoN, with differences within the stan- dard eиror across languages (fig.) 9 To provide a more statistically reliable comparison, we evaluate on the larger MMATH benchmark across 10 languages. On MMATH. FUSION achieves 85.1% accuracy averaged across 10 languages, outperforming BoN at 84.0% (+1.1%, ). Per-language results (fig. [10) show that FUSION matches or exceeds BoN in out of 10 languages. The Oracle upper bound of 90.6% indicates room for improvement for both methods on this benchmark.
Luo et al. 2025), which contains 374 examples per language
https:
a arena arena-hardee hard-hhard-v2.0
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100
10
Accuracy
90
Engish
German
French
Spancs
Russian
Japaness
Chinsse
Figure 9: Test-time Performance on MGSM. We find that BoN has a slight but not strictly signifi- cant advantage for 5/6 languages (within standard error). The performance of both methods is close to the Oracle.
100
121
English
Spanish
Portuguese
Vistrammea
Diate
NARS
Figure 10: Test-time Performance MMATH. Accuracy of BoN, FUSION, and the Oracle across 10 languages (N=5, T=0.7) with Command and four teacher models (~^374 examples per language). Error bars show standard error. FUSION matches exceeds BoN in of 10 languages.
further investigate two targeted modifications for math reasoning. First, we test an adaptive reward-score filter that removes candidates scoring below 5% threshold relative to the pool max- imum before passing them to the fusor. This pre-filtering yields minor improvements 7 languages MGSM, as detailed in table 7 Second, ablate the fusion prompt design MMATH. testing both a math-specific prompt (c.g., emphasizing reasoning correctness) and trans- lating the prompt into the target language. Neither modification yields statistically significant change, with all variants within + % of the baseline 72.8% accuracy, see table
out of
Taken together, these results confirm that FUSION works of the box for mathematical reasoning without requiring task-specific prompt tuning. The magnitude of gains over BoN is smaller than observed in translation, open-ended generation and world knowledge reasoning tasks, where output quality is more compositional.
E.2
SYNTHETIC DATA GENERATION
100
20.4
Accuracy
80
Fusion
Figure 11: Comparison of downstream performance on close-ended tasks MGSM and Geo- FactX. We find while on math BoN has the best performance, FUSION has higher accuracy on GeoFactX, outperforming the fusion judge as well. Error bars show std-err averaged across lan- guages.
Reasoning tasks Figure compares the performance on the two reasoning tasks for the models trained FUSION vs BON in relation the performance of the JUDGE model, i.e., the fusor
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Language
FUSION
FUSION + Adaptive
A
fr
85.2
86.8
+1.6
90.8
92.0
+1,2
ru
94.0
94.4
+0.4
Ja 90.0
90.4
+0,4
zh
90.0
90.4
+0.4
Table 7: MGSM accuracy (%) for Command A with and without adaptive reward-score filtering (5% threshold) on languages where the filter has an effect (N=5).
Method
Accuracy
Std. Err.
FusioN
72.8
2.3
FusioN + Math Prompt
72.9
2.3
FusioN + Math Prompt + Target Language
72.8
2.3
Table 8: Fusion prompt ablation MMATH: test-time scaling accuracy (%) with Command A across 6 languages (N=5). Neither math-specific nor localized prompts yield significant improve- ments.
model (COMMAND A) and also the BASE model that we start fine-tuning from. For MGSM, we find that the FUSION accuracy lags behind both BoN -1.2% and the JUDGE performance of -1.5% indicating that while FUSION is overall beneficial for improving downstream math accuracy (+3.6% above BASF), it not the optimal choice this case (with sligthly more pronounced gap than for the test-time scaling experiment in appendix E. But the facts that (1) BoN does not improve the performance of the fine-tuned model beyond the fusor's performance, and that (2) the baseline model performs already surprisingly strong, make us wonder whether these results could also be due to the interplay between data seen in prior training steps, in fine-tuning, and also the fusor model. Since the s1k dataset is quite popular, it might have been part of training (in English) of the fusor and the baseline already. For the factual QA domain we see stark contrast, that with clearly unseen data, FUSION effects stand out more. The model fine-tuned FUSION achieves the best accuracy with + +1.0% gains over BoN and an impressive +4.8% compared to fusor JUDGE.
F CONTRIBUTION ANALYSIS
Measuring contributions We inspect FUSION outputs and compare them with the teacher outputs with string matching. While this does not capture semantic rephrasings, it does give idea how much the fusor can directly copy and paste blocks of the teacher outputs. Parts of the FUSION output that we cannot directly find in any teacher's output, we mark "unmatched" we might have some close semantic matches or also just some "glue" work to connect parts from different teachers. The matching procedure works as follows:
where
1. Finds all matching blocks between the fused string and each teacher string. We make use of the difflib library"" and use their SequenceMatccher to detect the longest contiguous matching subsequences.
2. Resolve attribution for each character: Retrieve matching blocks that cover it, assume the teacher with the longest match wins. If there a tie: mark as "multiple". If there is no match, mark it as "unmatched""
3. Calculate contribution statistics for each teacher: Count how many characters of the fused generation attributed
Disentangling fusor and teacher pool BoN selections that chooses one teacher for each sequence. Figure we do this on small subset of the UFB data mix covering languages Arabic, English and Chinese. We look at pool of teachers
The resulting contribution statistics can be compared with
hhttps://dcce "httee::/dcc..pyytoo.orrrr http://///oco........oo/////////
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100
9.4%
Percentage
80
12.2%
9.7%
19.0%
11.6%
12.0%
20.5%
60
19.2%
44.4%
25.3%
46
44.8%
16.04 16.0%
14.8%
Arebic
English Contribution
Chinese
Arabic
English
Chinese
Kimi-K2
Owen3
Multiple
Seep5e
Gemma3
Unmatched
FUSTON contributions without fusor in the teacher pool: Analysis of different teach- ers the pool contribution to the final output of FUSION when the fusor model not in the pool. We look at FUSION outputs subset of UFB (50 samples per language)
Figure 12:
that does not include the fusor (COMMAND A) to study the effect of the fusor self-bias. Similar to what we found in Figure&whers FUSION and BoN had their highest contribution from COMMAND A (the teacher that we now removed), in Figure T2 the methods also have same preference, agreeing on DEEPSEEK-V. V3 as their favorite (previously second-ranking when COMMAND A was in the pool). This consistent preference lets us conclude that FUSION does not suffer from self-bias with the fusor able to reliably find the best samples the pool, whether or not the fusor samples is one of them.
185
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(a) Across AYA EXPANSE 8B and COMMAND A
(b) Across samples from COMMAND
Figure 13: FUSION contributions in test-time scaling: Analysis of how different samples in test- time scaling contribute to the final output of FUSION: (a) with different candidate models (b) based
on samples order. We look at FUSION outputs on subset of mArenaHard-v2. (50 samples per language)
Contributions in test-time scaling In Figure 13 we perform the contribution analysis on the test- time scaling setup, where the samples the pool are coming from a single model. First, we examine the effect of changing this single model on the FUSION preference. We are mostly interested in the case when the fusor much larger than the candidate model one would assume the fusor may opt to replace all of the weaker model outputs with its preference in the fusion which would result in higher unmatched rate. However, in Figure 13a we see that for both the small candidate model AYA EXPANSE 8B and the larger COMMAND A the fusor has small unmatched rate. Albeit larger for AYA EXPANSE 8B, demonstrate the fusor outputs are almost always more than 80% from the content in the samples.
We consider another type of possible bias in Figure 13b where we visualize the contribution analysis based on the order the samples in the FUSION prompt. The samples are always shuffled before being formatted (see Table 4 and we analyze the order based what the order the fusor sees. We find while not large magnitude, across the three languages, the fusor outputs have the highest contribution rations in the first and second samples.
Position bias
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Can comedian's 's of audience interaction enhance their delivery and stage presence?
Owen3=====
Yes, comedian's use audience interaction can significantly enhance both their delivery stage presence. When used effectively, audience interaction can transform a standard stand-up performance into more dynamic, engaging, and memorable experience. Here's 's how:
#*# Enhances Delivery
1. Creates Natural Rhythm and Pacing
Interacting with the audience can break up the monotony of scripted set, allowing for natural pauses, laughs, and transitions. This variation in pacing can make the performance feel more spontaneous and alive.
Provides Real-Time Feedback
Audience reactions during interactions give the comedian immediate feedback, helping...
Absolutely! A comedian's of audience interaction significantly enhance their delivery stage presence. powerful tool, and when done well, huge part of what separates a good comedian from great one. Here's breakdown how it works, and henefits:
How Audience Interaction Enhances Delivery & Stage Presence:
Breaks the Fourth Wall: It immediately makes the performance less of one-way lecture and more a shared experience. This creates more intimate and engaging atmosphere. Improves Timing & Reactivity: Good audience work forces comedian to present* and react in the moment. This sharpens their comedic timing, as they have to...
Deeepeeeee--------eeeeeee- V3 V3=====-
Absolutely! A comedian's use of andience interaction can significantly enhance their delivery and stage presence in several ways:
### 1. Engagement & Connection
Direct interaction makes the audience feel involved, creating personal connection that boosts engagement.
It breaks the "fourth wall," making the performance feel more spontancous and alive.
Adaptability & Improvisation
Skilled crowd work demonstrates quick wit, making the material feel fresh and unique to each show.
It allows comedians to riff off real-time reactions, adding unpredictability and excitement.
### Energy & Authenticity
dynamic back-and-forte keeps energy high...
Commandd==
Absolutely, a comedian's 's use of audience interaction can significantly enhance their delivery and stage presence in several ways:
Engagement and Connection: Interacting with the audience creates of inclusion connection. When a comedian involves audience directly. breaks fourth wall makes the performance feel more personal and intimate. This engagement can lead to more receptive and responsive crowd, which in turn boosts the comedian's 's confidence and energy on stage.
Example: Asking for volunteers incorporating audience members into bits orable moments that resonate with entire crowd.
Improvisation and Adaptability: Au...
Figure 14: An excerpt of the outputs of teachers to the FUSION example show in Figure
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Published as a conference paper at ICLR 2026
Can comedian's 's use of audience interaction enhance their delivery and stage presence?
Yes, comedian's use of audience interaction significantly enhance both their delivery and stage presence. When executed skillfully, it transforms standard stand-up performance into a dynamic, engaging, and personalized experience. Here's how:
### Enhancing Delivery
1. Improves Timing & Reactivity: Audience interaction forces comedians to be present and adapt real-time, sharpening their comedic timing and improvisation skills.
2. Creates Natural Rhythm: It breaks up scripted material, adding spontaneity and keeping the performance lively.
3. Allows for Callbacks & Customization: Referencing earlier interactions or tailoring jokes to
the crowd's responses makes the material feel fresh and relevant.
### Boosting Stage Presence
1. Builds Connection & Trust: Breaking the "fourth wall" fosters intimacy and makes the au- dience feel valued, strengthening the comedian's 's presence.
2. Showcases Confidence & Control: Handling unpredictable responses with humor demon- strates quick thinking and professionalism.
3. Humanizes the Performer: Sharing moments with the audience makes the comedian more
relatable and approachable.
### Types of Audience Interaction
-Call & Response: Builds energy and involvement.
Heckle Responses: Risky but rewarding when handled with wit.
Playful Banter: Engages specific audience members while maintaining sensitivity.
Improvised Bits: High-level crowd work that showcases exceptional skills.
### Potential Pitfalls
-Bombing: Interactions fall flat, killing momentum.
Offending Someone: Missteps can alienate audience members.
Losing Control:
Disruptive audience members can derail the performance.
Forcing It: Unnatural interactions feel awkward and contrived.
### Great Examples
-Dave Chappelle: Masters crowd work with charm and wit.
.Jerry Seinfeld: Uses subtle, observational interactions seamlessly.
Hannah Gadsby: Deepens emotional impact through audience engagement.
### Conclusion
When done well, audience interaction is a vital tool that elevates comedian's 's delivery, strength- ens their stage presence, and creates memorable, shared experience. requires practice, sen- sitivity, and good read of the room, but the rewards well worth the effort.
Figure 15: Contribution analysis for sample FUSION ouput with text colored based on the respec- tive source: CommandA, DeepSeck-V3. V3, Qwen3, Gemma3, multiple, and unmatched. Individual teacher generations are in Figure
Finally Figure 14 and we provide an example prompt with excerpts from teachers outputs, and in Figure 15 show the full FUSION output color coded according contribution analysis.
G DOWNSTREAM RESULTS FOR A SMALLER MODEL
To evaluate the effectiveness of method and generated data across different scales, applied our synthetic data generation and SFT pipeline to a smaller baseline. We followed the same setup as used for the 11 11 IB model. The 7B baseline is base model that have undergone any post-training
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mArenaHard- V2.0
WMT24++
Language
(win-rate, in %)
(XCOMETXL: en
(+region for WMT)
BoN
FUSION
A
BoN FUSION
A
ar (SA)
18.3
15.6
-2.7
65.4
66.3
+0.9
de (DE)
16.8
16.8
0.0
86.8
87.1
+0.3
en
14.9
14.1
-0.8
es (MX)
19.6
17.6
-2.0
81.0
81.5
+0.5
fr (FR)
22.4
17.2
-5.2
77.4
77.5
+0.1
(IT)
19.4
17.2
-2.2
80.1
80.4
+0.3
(JP)
17.0
19.5
+2.5
70.8
72.1
+1.3
ko (KR)
17.9
14.0
-3.9
72.0
72.5
+0.5
pt (PT)
16.9
16.6
-0.3
79.8
80.2
+0,4
ru (RU)
12.8
19.0
+6.2
74.8
74.5
-0.3
zh (CN)
19.8
20.9
+1.1
70.9
71.6
+0.7
Arg
17.8
17.1
-0.7
75.6
76.4
+0.5
Table 9: Downstream evaluation results on 7B Models of BoNiFusION-fine-tue 7B models
on mArenaHard- V2.00 (win rate against PRR as judged by GPT-40) and WMT24++ (X COMETXL..
Reasoning Score (LLM score. %)
Answer Correctness
(Accuracy, %)
BON
FUSION
Baseline
Fusor
BoN
FUSION
Baseline
Fusor
en
70.3
71.6
+1.3
66.5
69.2
69.3
72.1
+2.8
75.2
73.2
62.9
62.5
-0.4
54.3
61.6
49.6
48.0
45.1
$1.6
66.1
66.8
+0.7
56.7
61.6
61.0
61.7
+0.7
57.5
59.3
sw
58.7
61.5
+2.8
31.7
44.8
53.6
58.0
+4.4
35.3
44.0
47.6
47.8
+0.2
34.2
39.4
28.5
31.3
22.8
25.6
Arg
61.1
62.2
+0.9
48.7
55.3
52.4
54.2
+1.8
47.2
50.7
Table 10: Downstream evaluation on multilingual factual reasoning, as measured on the Geo- FactX test set. FUSION outperforms BoN both in terms of reasoning quality and answer correctness, with the exception of Hindi. Baseline is model used to finetune and Fusor the model used for fusing the generations.
stages. As shown in Table the downstream results vary. While FUSION remains more effective than BON in WMT24, improving performance in every language, we observed no gains significant over BoN in mArenaHard-v2.00 This suggests that the smaller model requires more parameter tuning to achieve an optimal setup for SFT to be effective in downstream performance, especially the relatively small size of out synthetic dataset.
We also report the detailed breakdown of results on downstream gains in the multilingual factual reasoning benchmark from the finetuned 11IB model in table I0 The Fusor and Baseline help anchor the gain and provide context about the magnitude of the finetuning gains in general. We find that fine-tuning consistently improve performance over the baseline with one exception in Hindi. More importantly FUSION also outperform the Fusor in most results.
In Figure 16 we move from comparing against GEMINI2.5 5 5-RRO as our reference and directly evalu- ate FUSION against BoN in pairwise head-to-head setup judge by GPT-40. We use AYA EXPANSE and COMMAND to generate samples on Arena, and aggregate with either one of our meth- ods. We find significant gain that resemble what we observe in Figure 2 FUSION outperform BoN across languages with large gains in magnitude for AYA EXPANSE 8B and lower but also impressive margins in for COMMAND A (up to +55.2% win-rate in Italian).
We perform a similar comparison Figure 17 between the finetuned models at the 11IB and 7B. We varying results across languages in direct head-to-heat comparison between the FUSION finetuned and BoN finetuned 11IB models. For the 7B we see that BoN scores better across all languages.
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520
AyaBb
40
23
4wman
IWech
Spanio
Chieese
Autoguota
Figure 16: Test Time Scaling Head-to- Head Comparison of FUSION vs BoN on mArenaHard-V -2, Judged by GPT-40. We consistently see that FUSION results in better final sample compared to FUSION for both AYA EXPANSE 8B and COMMAND A.
1138 Finetuned Models
20
78
Figure 17: Synthetic Data Finetuning Head-to-Head Comparison of og the models finetuned with FUSION vs BON on mArenaHard-V3 Judged by GPT-4o. We see varying results across languages the IIB case, while BoN is better the 7B level.
English
German
55
Win Rate
45
35
FusioN BoN
FusioN
BoN
Chinese
French
Win Rale
2
7 101112131415 2 Scale (N0
7 8
10 11 12 1314
Scale (N)
Figure 18: Head-to-Head Comparison of FUSION BoN on mArenaHard V2, Judged by GPT- 40. We consistently see that FUSION results in better final sample compared to FUSION for both AYA EXPANSE 8B and COMMAND A.
In Figure 18 we provide breakdown on the scaling plots (win-rates on marenaHard- V2.0 vs 1EMINI2.5-PRO across languages. We can see that in all subplots that FUSION grows faster than BON, with magnitudes depending on the language.
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FUSION's synthesis over selection paradigm makes more synergistic with improved sampling meth- ods, as it leverages higher-quality samples more ef- fectively than BoN. In Table [T] we compare both methods in the test-time scaling setup by measur- ing the win-rate of N samples against a single greedy sample across languages. The 5 samples are generated at temperature 0.7 with Hedged Sam- pling (Khairi et al. (025) and min-p sampling (Minh) er al.) 2024). The results show that FUSION bene- fits substantially more from improved sampling than BON, achieving more than twice higher win-rate gains (+33.0 vs. +12.5 for AYA EXPANSE 8B, +16.6 vs. +5.0 for COMMAND A).
Model
BoN
FUSION
Aya-Expanse 8B
+12.5
+33.0
Command A +5.0
+16.6
Table 11: Win-rate improvement over greedy (%) with Hedged Sampling and min-p on mArenaHard-V2, averaged across 7 lan- guages with N
We conduct additional experiments on WMT to examine why FUSION surpass not only BoN but also the oracle by design (Section 4 We compute BLEU as complemen- tary MT metric (as implemented in sacrebleu (Post 2018). with to- kenizers configured per target lan- guage) for the test-time scaling ex- periments with COMMAND A re- ported in Figure 3 Table T2 shows that FUSION outperforms the oracle 4 out of languages aggregate (de, ja, ko, zh). The rightmost col- umn reports the percentage of test sentences where FUSION surpasses the oracle, which ranges from 12% (es, fr) to 36% (ko), demonstrating that FUSION can improve upon the best individual sample in a non-trivial fraction of cases across all languages.
Language
Oracle
BoN
FUSION
de
29.39
24.98
29.77 (26%)
es
41.41
36.65
39.06 (12%)
fr
38.00
33.00
35.63 (12%)
it
34.86
28.43
34.20 (17%)
ja
22.61
17.35
22.80 (34%)
ko 24.61
18.54
25.41 (36%)
pt 27.94
24.01
26.92 (14%)
ru 21.13
16.96
21.09 (28%)
zh
36.92
26.59
37.68 (34%)
Table 12: BLEU scores (sacrebleu) for COMMAND A test- time scaling on WMT. Oracle selects the sample with the highest sentence-level BLEU. Percentages indicate the frac- tion of sentences where FUSION surpasses the oracle. Bold marks cases where FUSION exceeds the oracle in aggregate.
H ENSURING ROBUSTNESS IN EVALUATIONS
In Tables 13 and 14 we provide a detailed breakdown of the test-time scaling win-rate results from Section 4 The tables report the number of samples evaluated in each language, the win rates of FUSION and BoN, as well as the corresponding confidence intervals (CIs) and standard errors, computed according to the method proposed by MMiller 2024).
Although our internal RM is a strong baseline to use for BON, we provided additional comparison of FUSION vs BoN using an open-source reward model for better transparency and reproducibility of our work. To this end, we use open-source reward model on RewardBench2 at the time of writing. In table Table [5] we report test-time scaling win-rate results (N=5) for both the internal reward model and the open Skywork- V2 on mArenaHard- V2, averaged across 11 languages. These results confirm FUSION's superiority compared to BoN under SOTA reward models and enhance reproducibility.
1-BB the currently leading
We use GPT-4o as the judge in our LLM--s-judge evaluations, as it has been shown to have the high- est correlation with human judges across languages in recent work Kocmi et al. (202)). However, relying on a single LLM judge still carries a risk of bias that might affect our results. To address this concern, we repeat our test-time scaling evaluation on marenaHard- V2.0 with Claude Opus 4.1 as a second judge for a subset of languages. Table [6] shows the win-rate differences between FUSION and BoN in the test-time scaling setup where compare against GEMINI2.5-PPr The signal is consistent across both judges: FUSION outperforms BoN in three out of four languages under both judges, with varying magnitudes.
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Lang (# pairs)
Method
Win Rate
95% CI
Std. Err.
(479)
BoN
0.17
(-0.56, -0.70)
0.03
FusioN
0.24
(-0.44, -0.59)
0.04
de (420)
BoN
0.20
(-0.52, -0.68)
0.04
FusioN
0.29
(-0.33, -0.50)
0.04
(481)
BoN
0.16
(-0.60, -0.74)
0.03
FusioN
0.20
(-0.51, -0.66)
0.04
(463)
BoN
0.20
0.04
FusioN
(-0.35, -0.52)
0.04
fr (465)
BoN
0.20
(-0,51, -0.66)
0.04
FusioN
0.31
(-0.29, -0.45)
0.04
(462)
BoN
(-0.54, -0.68)
0.04
FusioN
0.27
(-0.37, -0.53)
0.04
ja (474)
BoN
0.21
(-0.49, -0.64)
0.04
FusioN
0.31
(-0.29, -0.46)
0.04
(474)
BoN
0.21
(-0.49, -0.64)
0.04
FusioN
0.30
(-0.31, -0.47)
0.04
pt (464)
BoN
0.19
(-0.54, -0.68)
0.04
FusioN
0,26
(-0,38, -0.54)
0.04
mu (472)
BoN
0.21
(-0.50, -0.65)
0.04
FusioN
0.29
(-0.31, -0.48)
0.04
zh (425)
BoN
0,23
(-0.45, -0.61)
0.04
FusioN
0.31
(-0.28, -0.46)
0.04
Table 13: Win-rates with 95% confidence intervals and standard errors for Aya-Expanse 8B Gemini-2.5 Pro on mArenaHard-V222 (N=5).
I COMPUTE EFFICIENCY ANALYSIS
We discussed sample efficiency of FUSION relative to BoN in Section below provide ana- lytical FLOP-based comparison showing the computational trade-offs between the two approaches.
Framework for comparison. Following the scaling laws of Kaplan et al. 2020), the number of FLOPs required for a single forward pass with context length L as
approximate
FLOPs (L) 2P + Ld,
where Pne denotes the number of non-embedding parameters, ne the number of layers, and d the model dimension. To generate T output tokens, we multiply FLOPs by T.
Assuming the same model architecture and size for the fusor and the reward model, we estimate FLOPs as function of the expected sample length and the choice of N, Sample length varies substantially across tasks (e.g., single sentences in machine translation versus multi-line code or longer documents in MarenaHard). We assume that FUSION does not significantly increase the final output length compared to the selected sample (as verified on mArenaHard),, additional tokens for evaluation reasoning. We model this by reasoning factor such that the fusor generates approximately T = rL, tokens for sample length L, (c.g., r 1.2).
produces
BoN requires N forward passes with inputs of length L., each producing single output token (for simplicity; in practice a projection layer may replace the embedding layer). The total FLOPs are
FLOPP N. FLOP (La)-
FUSION performs single forward pass with concatenated input of length NL,, and generates approximately T rL, output tokens. The total FLOPs are therefore
FL.OPSrusion rh., FLOOP (N
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Lang (# pairs)
Method
Win Rate
95% CI
Std. Err.
(479)
BoN
0.41
(-0.08, -0.25)
0.04
FusioN
0.40
(-0.08, -0.26)
0.04
de (420)
BoN
0.42
(-0.06, -0.24)
0.05
FusioN
0.51
(0.14, -0.05)
0.05
(481)
BoN
0.39
(-0.13, -0.30)
0.04
FusioN
0.48
(0.08, -0.09)
0.04
(463)
BoN
0.45
(0.01, -0.17)
0.05
FusioN
0.53
(0.18, -0.00)
0.05
fr (465)
BoN
0.43
(-0.03, -0.21)
0.05
FusioN
0.45
10.03,00.14
0.05
(462)
BoN
0.39
(-0.11, -0.28)
0.05
FusioN
0.45
(0.01,
0.05
ja (474)
BoN
0.41
(-0.08, -0.25)
0.05
FusioN
0,42
6-0.07.0.224
0.05
(474)
BoN
(-0.02, -0.19)
0.05
FusioN
0.45
(-0.00, -0.18)
0.05
pt (464)
BoN
(-0.12, -0.30)
0.04
FusioN
0,44
(-0.02, -0.20)
0.05
mu (472)
BoN
(-0.12, -0.29)
0.04
FusioN
0,43
(-0.03, -0.21)
0.05
zh (425)
BoN
0.47
(0.04, -0.15)
0.05
FusioN
0.44
(-0.03, -0.22)
0.05
Table 14: Win-rates with 95% confidence intervals and standard errors for Command A vs. Gemini- 2.5 Pro on mArenaHard-V22. (N=5).
Model
FusioN
Skywork- V2 BoN
Internal RM BoN
Aya-Expanse 8B
27.86
18.54
19.84
Command A
45.40
39.64
41.62
Table 15: Test-time scaling win-rates (%, vs. Gemini-2.5 Pro) averaged across 11 languages on mArenaHard- V2.00 with N-5.
Under this formulation, FUSION FLOPs grow linearly with L, through the output term and with NL, through the context length, whereas BoN FLOPs scale linearly N and are independent of any additional output-length factor. As discussed in Section FUSION involves one step of sequen- tial scaling, while BoN leverages parallel scaling across samples. This can be disadvantageous in compute-restricted or latency-sensitive settings. At the same time, it can be reasonable to allocate more compute to the selection process for longer inputs. This consideration also motivated our ex- periments on synthetic data generation, where investing additional compute in optimizing sample quality leads to improved downstream performance.
Wall-clock time and monetary cost depend on specific model implementations and hardware con- figurations. The above comparison therefore focuses on analytical FLOP estimates to clarify the computational trade-offs.
J EXTENDED RELATED WORK
Learning from Ensembles The principle of learning from ensembles has led to advances in many
areas of machine learning, and can be integrated into training LLMs in various forms: For example, Huang et al. (2024) fuse multiple models via their output probabilities, Lee et al. (2023) learn from consensus of multiple teachers in self-instructing (Wang al., 2023b). and Wan et al. (0024)
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Language
Judge
Difference (FUSION BON)
de
Claude-4.1
+1.4
GPT-4o
+9.5
en
Claude-4.1
+2,9
GPT-4o
+9.6
ar
Claude-4.1
+0.8
GPT-4o
1.0
zh
Claude-4.1
+1.6
GPT-4o
-3.5
Table 16: Win-rate difference (FUSION - BoN, %) against Gemini-2.5 Pro for Command A on mArenaHard-Ve V2.0 under two judges.
propose a continual pretraining objective for knowledge distillation from multiple teachers. In this work, we focus on integrative output ensembling, where we simply provide LLM (the fusor) the ensemble of outputs input to integrate their strengths into a fused output.
Synthesis-based ensembling Our approach can be seen as an instance of Mixture-of-Agents (MoA) (Wang et al. 2024), a framework where multiple agents organized in layers iteratively enhance the output. Our approach stands out through simplicity: We show that FUSION becomes effective already in single aggregation step with single fusor, even in diverse and challenging setups, thereby constituting an attractive alternative for BoN, which thanks to its simplicity- much more widely adopted framework than MoA. It is also worth noting that improvements the sampling step in ensembling methods are directly applicable to FUSION, as they are orthog- onal the synthesis framework. These improvements target either sampling efficiency, such as pruning-basee approaches proposed in Wang et al. (2025); Qiu et al. (2025), or sample quality, as explored in Minh al. (2024): Khairi et al. (2025) We provide additional experiments the latter in Appendix E
LLM- LLM-Blender (Jiang et al. 2023) follows a similar idea, but requires two separate modules. one for pairwise ranking, and one for fusing top-ranking outputs. In contrast to our work, this framework operates on the basis of pairwise comparisons (which require training specialized model), while we argue that the fusor should receive all outputs at once to best comparatively evaluate them.
Other contemporaneous related works also require training such specialized aggregator modules QQi et al.] 2025) Zhao et al., [225]] Li et al. (0025)), while our approach is effective without any truining. These works focus primarily on verifiable tasks like math and code targeting RL or reason- ing. For such specialized scenarios with expert models available, [i ct al. (2025a) warn that MoA might not be sufficiently robust to lower-quality inputs. For the more diverse generative evaluation scenarios that
targeting, however, we find that FUSION is fairly robust with respect to the teacher pool (section 5). and sampling from a single teacher-thh the proposed solution by Li et al. (025a) performs significantly worse. Payalath et al. (2025) find that fused single-teacher roll- outs can nevertheless provide valuable supervision in RL training, even without any fusor training. Overall, our work fits nicely in stream of very recent developments discovering new possibilities of synthesis as part of the inference process. Even though the idea of FUSION is so intuitive and shared among recent works, our work advances the understanding of the inner workings and limitations of this principle. We show that implemented even in its simplest form, it brings gains in highly diverse applications for both at test-time and for driving model supervision.
Test-time scaling Our approach can also be cast as combination of parallel and sequential test- time-scaling (Welleck et al. 2024; Snell et al., 2025), with N parallel steps and one refinement step. Inoue et al. (2025) formulate this combination as a search problem where in each step either more samples can be requested, or existing ones can be revisited. This poses an interesting avenue for future work, where FUSION operates with adaptive compute (rather than a fixed N+1) based on each input sample. This flexibility might be needed for attempts to mimic human cognitive processes more closely (Zhang et al. 2024
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Synthetic data generation In the development of multilingual LLMs in particular, synthetie data generation has played a core role to reduce language disparities. For example, two recent models Apertus (Hermández-Carl Cano et al. 2025) and EuroLLM (Martins et al. 2025). rely EuroBlocks! collection of synthetic fine-tuning data obtained from various sources and individual teachers. Such synthetic data has also been key in improving mathematical reasoning, both monolingually (Muen- nighoff et al. 2025) and multilingually (Lai & Nissim 2024] Hwang et al., 2025). Involving and en- sembling multiple generations from either the same or multiple teachers in the process, we study here, is still underexplored. For Llama 3, Grattafiori et al. (2024) report using rejection sampling (i.e. BoN) for multilingual data generation. For Aya Expanse, [Dang al.] (2024) report routing samples to multiple teachers (Lu et al., 2024) via multilingual BoN as proposed in (Odumakinde) al. 2025), a strategy also adopted for building Tower+ (Rei et al. 2025).
K
LLM USAGE DISCLOSURE
In this paper, we used Al in several auxiliary functions:
Formatting of result tables in I8TEX.
Shortening the text fit into space limits.
Polishing text by finding English correspondences to our non-English ideas.
Implementation aid for the contribution analysis.
Expansion of our initial list of related works, which we then read and carefully curated into the final related work discussion.
http:///wwgaaaeeeeeeeee/ee/e/tsp 112
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Reviews:
[{"review_id": "wsRygkOvpP", "reviewer": "Reviewer_jD1s", "summary": "The paper proposes Fusion-of-N (FUSION), a paradigm that replaces the traditional Best-of-N (BON) selection strategy for LLM responses. Instead of picking a single “best” candidate among N sampled outputs, a fusor model synthesizes a new response by combining the strengths of all N candidates. The authors claim this approach better utilizes complementary information among responses.\nThey evaluate FUSION in two scenarios: (1) test-time scaling—sampling multiple responses from a single model and fusing them; and (2) data synthesis—fusing outputs from multiple teacher models to generate training data for a student model. Experiments span several multilingual and multi-domain tasks (open-ended generation, translation, factual QA, and math reasoning). The paper provides a universal prompt template for fusion and reports that FUSION outperforms BON on most tasks, though BON remains stronger on MGSM (math reasoning).", "questions": "1. What reward model and settings were used for BON? Would a stronger public RM or combined judge close the reported gap?\n\n2. Can you provide human or multi-reference evaluations showing that FUSION genuinely produces higher-quality translations than the best candidate?\n\n3. Why does FUSION fail on MGSM? Is it due to logical reasoning disruption, verbosity, or hallucination?\n\n4. Please report the average input length, latency, and cost per query for N={2,4,8}.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "1. The paper identifies a real limitation of BON, the inability to exploit complementary information between candidates—and argues for synthesis rather than selection as a reasonable alternative.\n\n2. The method is lightweight: it requires no additional training or fine-tuning, only a general “fusion prompt,” making it reproducible and accessible for practitioners.\n\n3. The authors openly report cases where FUSION fails (e.g., MGSM), which adds transparency.", "weaknesses": "1. The proposed method is largely a prompt-level heuristic. The core idea of asking a strong LLM to compare and fuse multiple outputs, is conceptually simple and closely resembles prior response aggregation or mixture-of-agents paradigms. The paper mostly offers a new name and systematic evaluation rather than a substantive algorithmic innovation.\n\n2. The BON baseline uses an unspecified reward model and selection protocol. Details such as training data, tuning strategy, and model scale are unclear, making it difficult to judge fairness. The authors only compare against small-N BON (typically N=5), omitting modern BON improvements (hierarchical sampling, de-duplication, or diverse beam reranking). All comparisons rely on a single LLM judge (GPT-4o), introducing possible evaluation bias.\n\n3. The paper claims FUSION sometimes surpasses the oracle best candidate (highest XCOMET score among N). However, this “oracle” is defined by an automatic metric; it is not clear that human annotators would agree. This undermines one of the paper’s central claims.\n\n4. The fusion step concatenates N long answers into a single context, increasing token length and making inference sequential and costly. The authors briefly mention sample efficiency but ignore wall-clock latency, memory, or dollar cost. In contrast, BON’s selection is trivially parallelizable.\n\n5. FUSION underperforms BON on verifiable reasoning tasks like MGSM but outperforms on subjective or open-ended tasks. The paper fails to explain why or to propose an adaptive choice mechanism.\n\n6. Input formatting (order of candidates, truncation, token limits, multilingual mixing) is under-specified. The authors mention minor order bias but do not quantify its effect.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "d5WP0H4alg", "reviewer": "Reviewer_digP", "summary": "This paper argues that the dominant Best‑of‑N (BON) framing of generate N candidates and select one throws away useful signal. The authors propose Fusion‑of‑N (FUSION): a training‑free, prompt‑driven synthesis step in which a fusor LLM reads the prompt and the N candidates and writes a new answer that integrates the best parts and discards the worst. \n\nThey evaluate FUSION in two settings that currently rely on BON: (i) test‑time scaling (sampling multiple candidates from the same model and aggregating), and (ii) synthetic data generation (sampling across a pool of teacher models and aggregating for SFT). Experiments span 11 languages, three task families (open‑ended generation on mArenaHard‑v2, WMT24++ translation, and factual/math reasoning), and two model scales (8B and 111B).\n\nFUSION consistently beats BON at the same sampling budget and, on WMT, sometimes exceeds the “oracle” single candidate scored against references, establishing that selection is not an upper bound. The paper further analyzes when FUSION works best (larger fusors, small N, diverse teacher pools) and where it struggles (close‑ended math)", "questions": "- Beyond the position‑bias probe, did you (a) repeat Arena comparisons with two heterogeneous judges and report agreement, or (b) conduct a small‑scale human‑blind study to validate GPT‑4o outcomes, as recommended by recent LLM‑as‑Judge surveys? Please add such a calibration or discuss why it’s not feasible.\n- Can you provide token‑level accounting for (i) sampling N candidates, (ii) concatenation length, and (iii) fusion generation—and compare end‑to‑end latency vs. BON at equal quality? This matters because FUSION’s long‑context pass is not trivially parallelizable, and likely adds significant user compute time\n- Table 3 notes slightly lower “language correctness” for FUSION, and the fusion prompt is English‑only (Table 4). Have you tried localized fusion prompts (per language) or a multilingual safety/style rubric? If so, do GeoFactX non‑English results improve?\n- What happens if 1–2 of 5 teachers inject subtly wrong facts or unsafe content? Does FUSION filter them or amplify them, and can adding a pre‑filter materially help?\n- Could you complement string‑matching with semantic span alignment (e.g., NLI‑based attribution or claim‑level voting) to better characterize “fusion” vs. “copy”? This will clarify whether FUSION is truly synthesizing or mostly selecting + rephrasing", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- With a single prompt, FUSION is drop‑in for selection and consistently improves win‑rates on open‑ended prompts and XCOMET‑XL scores on translation across 11 languages. The fact that FUSION sometimes surpasses the reference‑scored oracle in WMT (German/Russian/Chinese; Fig. 2, p. 5) is a sharp, convincing result showing synthesis > selection.\n- The paper evaluates both test‑time and data‑generation use cases, the two places BON is used most heavily today, and does so across 11 languages", "weaknesses": "- The attribution method (difflib) cannot detect paraphrastic reuse or semantic fusion. Also, the fusion prompt is English‑only, and Table 3 (p. 8) notes slightly lower “language correctness,” suggesting the prompt could disadvantage lower‑resource languages. A multilingual fusion prompt and a semantic contribution analysis (e.g., span‑level alignment or entailment) would strengthen claims about “polylithic” quality.\n- The core sales point is sample efficiency at small N, yet the paper does not report wall‑clock or token‑level costs for: sampling N candidates, concatenating them into a long‑context window, and running fusion. Since BON can parallelize N samples and fusion adds a sequential long‑context pass, end‑to‑end time/compute curves vs. quality are necessary to substantiate the “efficient” claim (authors briefly note this qualitatively, and Fig. 5 shows win‑rates only).\n- The authors observe a mild position bias (Fig. 11b, p. 25) and randomize candidate order, but do not explore robust mitigations (e.g., segmented fusion with random reshuffles, majority‑vote over multiple shuffled fusions)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IbnDFGlKvF", "reviewer": "Reviewer_xyhz", "summary": "This paper argues that the dominant “choose one winner” aggregation paradigm—Best-of-N or its variants like self-consistency—is wasteful and brittle because it discards potentially useful information present across candidate generations. The authors propose Fusion-of-N (FUSION): instead of selecting, a strong LLM synthesizes a final answer by integrating the best parts of N candidates. They evaluate FUSION in two common aggregation settings: (i) test-time scaling (multiple samples, one model) and (ii) synthetic data generation (multiple teachers, one student). Across 11 languages and three benchmark families (Arena-style open-ended dialogue, WMT24++ translation, and reasoning datasets like MGSM/GeoFactX), FUSION is reported to (a) improve mArena-v2 win-rates vs GEMINI2.5-PRO, (b) raise XCOMET-XL translation scores (even beating the oracle best sample on some languages), and (c) produce better synthetic datasets that yield downstream gains when used to fine-tune 7B/111B students.", "questions": "Q1 - Cost–quality trade-off: Can you plot final quality metric (win-rate vs GEMINI-2.5-PRO, XCOMET-XL, MGSM accuracy) against actual inference cost? For BON and FUSION at N = 1, 2, 5, 10? Include both FLOPs/token estimates and observed latency on your hardware. This would make the “FUSION is more sample-efficient” claim more actionable. \n\nQ2 - Robustness to adversarial / low-quality candidates: You test robustness to weaker teacher pools, but what if a candidate is malicious: toxic, off-policy, prompt-injected, etc.? Does the fusor tend to copy problematic spans, or does it filter them out? Any ablations where you intentionally inject garbage/noisy generations into the pool?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "### S1. Clear, compelling reformulation (selection → synthesis)\nThe paper articulates a neat conceptual shift: treat aggregation as synthesis rather than selection. The formalization and narrative around a polylithic view of quality (good and bad fragments within each sample) nicely motivate why a generative judge could outperform scalar scorers. The method is also simple to adopt: keep the same candidate pool, swap BON for a fusor prompt.\n\n### S2. Broad empirical coverage with multilingual focus\nThe evaluation spans 11 languages, two scales for test-time scaling (8B and 111B) and multiple downstream tasks. The work puts emphasis on multilingual open-ended dialogue (mArena-Hard v2.0), WMT24++ translation (scored by XCOMET-XL), and GeoFactX/MGSM reasoning—giving the reader a more realistic view of behavior beyond English.\n\n### S3. Strong results; synthesis can beat oracle in MT\nIn machine translation, FUSION beats BON by large margins and even exceeds the oracle (best single candidate given ground-truth references) in several languages (e.g., German, Russian, Chinese), establishing that generative fusion is not upper-bounded by selection. That is a crisp and important empirical takeaway.\n\n### S4. Test-time scaling wins and consistent synthetic-data gains\nOn Arena-style evaluation, FUSION improves win-rates against a frontier model, Gemini 2.5-Pro, using the same N as BON; e.g., +9.5% in German for COMMAND A with N=5. Fine-tuning on FUSION-selected data yields +2.5 Arena win-rate avg and +0.8 XCOMET-XL over BON-selected data showcasing that fusion not only helps at inference, but also transfers to training.\n\n### S5. Useful analysis of how synthesis works and when it fails\nThe paper inspects contribution at the character/segment level (difflib SequenceMatcher), showing the fusor often copies >80% from the presented candidates, rather than hallucinating wholecloth. Further, FUSION tends to reach BON’s quality using fewer samples. It also shows a certain size threshold—bigger fusors unlock better generative fusion—clarifying practical requirements. Moreover, the paper is candid about math being a weaker regime for out-of-the-box FUSION.", "weaknesses": "While I like the paper and it has strong contributions, I have a few minor suggestions. If my main requests are fulfilled (W1, W2), I would be happy to raise my score.\n\n### W1. Dependence on a single proprietary judge.\n\nHeadline results often rely on GPT-4o for LLM-as-judge preferences (for Arena-style head-to-heads). This raises a concern: Are fusor outputs advantaged by stylistic preferences of GPT-4o (e.g. verbosity, hedging tone), rather than objective quality?\nPlease report, at least for a subset of languages, human eval or a second independent judge to ensure robustness (and estimate judge bias if any).\n\n### W2. Reproducibility / openness.\n\nA key component of the evaluation, the internal RM for scoring BoN, is proprietary or unreleased. The paper promises to release fused vs BON-selected datasets “where licenses allow,” which is good, but full reproduction remains difficult. I would strongly suggest the authors to also use an open-source reward model to make the findings reproducible for the open-source community. Please also release (i) judge prompts, (ii) fuse prompts (Table 4 already helps), (iii) a BON baseline using only public RMs, so independent researchers can validate.\n\n### W3. Math / verifiability tasks expose a limitation.\n\nOn MGSM, BON outperforms FUSION both at test-time aggregation and after SFT. FUSION can actually degrade logical consistency by “cleaning up” reasoning steps in ways that introduce subtle arithmetic or chain-of-thought errors. This undercuts the “drop-in replacement” message: FUSION does not dominate BON in all domains, especially where correctness is binary and checkable.\n\nI would appreciate if the authors can be more direct in the main text. For example, something like: “FUSION helps open-ended generation and translation, but BON (plus verification) is still stronger for objectively verifiable tasks such as math.” \n\n### W4. Statistical reporting could be tighter.\n\nArena-style win-rates are given mostly as point estimates (+10.8, +9.5, etc.). The paper notes position biases and tie-handling, but doesn’t present full confidence intervals or absolute counts of judged pairs per language. Please add CIs or bootstrap intervals for all win-rates.\n\n\n\n\n## Presentation Nitpicks:\n\n### Typos / phrasing\n\n1. In the fusion prompt section (Table 4), I noticed minor spelling issues such as “pormpt” → “prompt”, and awkward phrases like “scaler rater,” which should probably read “scalar rater.” \n2. GeoFactX evaluation text includes a phrase like “agains the list,” which should be “against the list.” \n3. MGSM discussion: “math task compared BON” should read “compared to BON.” \n4. Figure captions / axis labels occasionally misspell “mArenaHard-v2” (e.g. “m-AreanHard-v2”). Please normalize. \n5. “XCOMETXL” vs “XCOMET-XL” appears inconsistent; use a single canonical form in text and figures. \n\n\n### Figures / presentation\n\n1. In Figure 1 / main win-rate barplots, please annotate $N$ (e.g. N=5 samples fused), identify the judge model, and the number of judged pairwise comparisons so readers can gauge statistical strength at a glance. \n\n2. Add error bars / confidence intervals for win-rates, or at least show per-language sample counts. Right now improvements are large, but we can’t tell which differences are stable and which are noisy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)