Predicting LLM Reasoning Performance with Small Proxy Model
Authors: Woosung Koh
Juyoung Suk
Sungjun Han
Se-Young Yun
Jay Shin
Venue: ICLR 2026 Poster (2026)
Keywords: Language Models
Pre-training
Reasoning
Evaluation
Efficiency
Primary area: foundation or frontier models, including LLMs
TL;DR: We enable small proxy models to reliably predict large model reasoning performance using next-token prediction on reasoning traces with task-aligned weighting, dramatically reducing pre-training recipe search cost.

Abstract: Given the prohibitive cost of pre-training large language models, it is essential to leverage smaller proxy models to optimize recipes before scaling up. However, this approach becomes challenging for reasoning capabilities, which exhibit \textit{emergent} behavior that only appears reliably at larger model sizes, often exceeding 7B parameters. To address this, we introduce \tsc{rBridge}, showing that small proxies ($\leq$1B) can effectively predict large-model reasoning by aligning more closely with \textbf{(1)} the pre-training objective and \textbf{(2)} the target task. \tsc{rBridge} achieves this by weighting negative log-likelihood with task alignment, using reasoning traces from frontier models as gold labels. In our experiments, \tsc{rBridge} \textbf{(i)} reduces dataset ranking costs by over 100$\times$ relative to the best baseline, \textbf{(ii)} achieves the strongest correlation across six reasoning benchmarks at 1B to 32B scale, and \textbf{(iii)} transfers predictive relationships across pre-training recipes at 1B to 7B scale. These findings indicate that \tsc{rBridge} offers a practical path for exploring reasoning-oriented pre-training at lower cost.

Published as a conference paper at ICLR 2026
PREDICTING LLM REASONING PERFORMANCE WITH SMALL PROXY MODEL
Woosung Koh " Juyoung Suk "*, Sungjun Se- Yun*", Jamin Shin
TTiilion Labs, KAIST Al
"Correspondence to: yunseyoung @ kaistac (sungjun.han, jay) @trillionlabs.co
ABSTRACT
Given the prohibitive cost of pre-training large language models, it is essential leverage smaller proxy models to optimize datasets before scaling up. However, this approach becomes challenging for reasoning capabilities, which exhibit emer- gent behavior that only appear reliably at larger model sizes, often exceeding 7B parameters. To address this, introduce BRIDGE, showing that small prox- ies (<IB) can effectively predict large-model reasoning by aligning more closely with (1) the pre-training objective and (2) the target task. RBRIDGE achieves this by weighting negative log- legliiklihhodd with task alignment, using reasoning traces from frontier models as gold labels. In our experiments, RBRIDGE (i) reduces dataset ranking costs by over 100x relative to the best baseline, (ii) achieves the strongest correlation across six reasoning benchmarks at 1B to 32B scale, and (iii) zero-shot transfers predictive relationships across pre-tree pre-training datasets at 1B to 7B scale. These findings indicate that RBRIDGE offers a practical path for exploring causoiigg-oriente pre-training at lower cost.
RBRIDGE
INTRODUCTION
Pre-training modern language models at scale requires enormous computational and data resources, making it infeasible to exhaustively explore pre-training design choices directly at large scale (Rad- ford et al., 2018; 2019; Brown et al., 2020; Dong et al., 2019; Hoffmann et al., 2022a; Cottier et al., 2024; Hu et al., 2024; Khandelwal et al., 2024; Han et al., 2025). In response, leveraging smaller models as a proxy for larger model performance has been a key direction through the establishment of empirical scaling laws for prediction (Kaplan al., 2020; Hoffmann et al., 2022b) derivation of pre-training dataset rank invariance across scale (Magnusson et al., 2025).
Small Proxy
pP (token) P
sum
madulo
V
The
number
has
rBridge log (token,)
Task Critical Tokens
Wi min(w)
Formatting/Crentt Creative Tokens
max(w) min(w)
Reasoning Trace
p (letter)
token,
n (token) 2
modulo
\n
The
number
has
pd (letter)
Figure 1: Schematic overview of RBRIDGE, which is used to predict and rank performance at much larger model size. We use a frontier model " reasoning trace (Wei et al., 2022b) as the gold label Y* and compute weighted NLL for evaluation. Each token i's NLL is weighted by the frontier model's confidence in that token (MinMax normalized). To handle tokenizer mismatches between proxy and frontier models, compute weights at the letter level and average within tokens.

---

Published as a conference paper at ICLR 2026
5
0.074
R 0.432
10-
328: 0.830
MATH500 Accuracy
MATH500
6-
4-
500
1000 1500
2000
2500
3000
500
1000
1500
2000
2500 3500
Pre-trained Tokens (8)
Pre-trained Tokens (8)
(a) Pre-training progress on IB and 7B model
(b) Pre-training progress on 13B and 32B model
Figure 2: Using MATH500 as an example benchmark, given the same data source OLMo-Mix- 1124 (OLMo et al., 2024), smaller models exhibit more noise and get the direction wrong, making it challenging to use smaller models to proxy larger model performance. R² values are derived from linear curve fitting. Extended visualization across other reasoning benchmarks are available in Appendix A.
However, the literature on the emergence of reasoning performance as we scale model size (Wei et al., 2022a; Almazrouei et al., 2023; Du et al., 2024) suggests that there may be limit to how small theses proxy models can be. They demonstrate that reasoning capabilities only appear when models are sufficiently large in size. Du et al. (2024)'s granular study demonstrates random accuracy on small scale models of size 300M 3B benchmarked on reasoning tasks like MMLU (Hendrycks al., 2021a) and GSM8K (Cobbe et al., 2021). Contrarily, other non-reasoning benchmarks like TriviaQA (Joshi et al., 2017) and HellaSwag (Zellers et al., 2019) show smooth signs improve- ment even small scale.
We further visualize this challenge of using small models to proxy large model especially for reason- ing in Fig. While larger models exhibit stable Accuracy (Acc.) improvement (Fig. 2b), smaller models are highly noisy, and in the case of the smallest 1B model, sloping in the wrong direction (Fig. 2a).
Due to this limitation, practitioners are often constrained to relatively larger proxy models up to 15B to capture reasoning performance, which incurs substantial computational and economic costs (Grattafiori et al., 2024; DeepSeek-Al et al., 2025). For example, single training run of 7B model with 500B tokens can reportedly exceed 50K USD cost (Han et al., 2025).
Contribution. To bridge the evaluation scheme at small proxy to large target scale, we first analyze limitations of past approaches ($ 3.1). Our analysis uncovers that existing methods fail to ($ 3.1 (1)) align with the pre-training objective, and ($ 3.1 (2)) align with the target task. (1) Alignment with the pre-training objective is required as small pre-trained models lack strong generalization capabilities. (2) Ensuring that the evaluation scheme is aligned with the target task is necessary to fulfil our ultimate goal of proxying task performance at large scale. To achieve (1, 2), use frontier-model generated gold reasoning traces (Wei et al., 2022b) for negative log-like likelihood (NLL) (Fig. 1). Then, further task alignment at the token-level by automatically weighting tokens based on their level of task-alignment (Fig. 1). We empirically validate our method RBRIDGE ($ 4) in the following five ways:
On a pre-training dataset ranking benchmark with target 1.2B scale, RBRIDGE achieves 80.8 % decision accuracy across 25 pre- pre-training datasets, outperforming 5 baselines, ducing dataset ranking compute cost by at least 100.2x against the best.
2. RBRIDGE achieves best average proxy (IB) to target (13B, 32B) relationship across benchmarks (mathematics, science, engineering, commonsense, and coding tasks), against 6 baselines.
3. RBRIDGE achieves best average IB - 13B relationship after a supervised fine-tuning (SFT) stage at target scale across 4 benchmarks, against baselines.
2

---

Published as a conference paper at ICLR 2026
More In- Distriibuution
More Dut-of-Distributtion
1.3
8.2
ARC-C: R' 0.003
8.0-
COA: R 0.018
1.2
7.8
1.1-
MATH500: R' = 0.940
HumanEval: R² - 0,919
7.6-
7.4-
500 1000
1500
2000 2500 3000
3500
500
1000
1500
2000
25000 3000
3500
Pre-trained Tokens (B)
Pre-trained Tokens (B)
(a) In-distribution Y provides smooth signal.
Out-of-Distribttion Y provides signal.
Figure 3: When evaluating a IB pre-trained model with next token prediction, T (2 v&r)) whether the target Y* is in-distribution becomes important. All visualized benchmarks demon- strate smooth improvements at larger (13x, 32x) scale with target metric Acc./p@k. For clarity, we visualize the benchmarks in our empirical study with the two smallest and largest average NLL values.
4. R BRIDGG outperforms proxy models 7 13x larger using the target metric (e.g. Acc., @ass@K).
5. Finally, we demonstrate that RBRIDGE-target relationship on one pre-trained dataset can be zero-shot transferred to an alternative dataset for low-error performance prediction and ranking using fraction of experimental compute cost.
PROBLEM SETTING
Let nP, n' denote small proxy and large target model, respectively. The target metric metric' (e.g., Acc., @ass@k) fixed. Our objective is to design proxy evaluation metric" such that considering metric" + metric', find metric" corr(.) function we use in future sections. That is, improvements observed at proxy scale should reliably predict improvements at target scale. We denote scaling experiments
maXmetre corr corr(metric(T²))
metrrcc(<T')). We discuss the
where n, m is the proxy. target model size, respectively: e.g., 1B - 32B. Performance changes can arise from either (I) varying the training dataset at fixed data size, (II) varying the training data size given a fixed dataset. This enables two key applications: (I) comparing alternative training datasets without training large models each, and (II) predicting whether scaling up training data (e.g., 3000B + 3500B tokens) is worthwhile. This is important because it enables us to predict the return on investment for large-scale training before committing resources. A practical proxy evaluation scheme must therefore be reliable at small scale and achieve high correlation.
BRIDGING SMALL AND LARGE MODEL SCALE EVALUATION
We find that small models become strong proxies for large models when achieving alignment along two axes: alignment with the pre-training evaluation objective ($ 3.1 (1)), and alignment with the target task ($ 3.1 (2)). In response, we introduce our method, RBRIDGE ($ 3.2).
3.1 PRIOR APPROACH LIMITATION
(1) Evaluation Objective Misalignment. We find that the first limitation of existing approaches their lack of evaluation objective alignment with лP. This is required as small pre-trained models lack strong generalization capabilities. Concretely, (a) Acc. and Pass @1 K (p@k) is misaligned with the pre-training objective function, and (b) even if we use an objective function aligned evaluation scheme like NLL., must stay aligned with the pre-trained model's distribution.
3

---

Published as a conference paper at ICLR 2026
(1.a) Misaligned Evaluation Metric.
Existing target metrics like Acc./p@k are misaligned with a
the proxy model's 's next token prediction (NTP) NI
1000s
Gold Label Y
Dataset
Y":
D
ScB /t0 A
Ressoning Trace R Average
Average MH op Y
Min NLL (L)
1.168
1.285
0.925
Average NLL (L)
1.228
1.351
0.981
Max NLL (4)
1.307
1.405
1.060
1B 13B
-74.7% decrease
Train R² (1)
0.861
0.814
0.886
Test MAE (4)
0,593
0.637
0.524
1B ++ 32B
Train Rº (1)
0.823
0.809
0.832
Test MAE ())
0.815
0.933
0.739
MATH500
GSMBK
MMLU
ARC-C
CQA
(STEM)
Table 1: We observe a direct relationship between Figure 4: Using reasoning trace R over bench- degree of in-distribution (as presented as NLL) mark test set's Y* significantly reduces NL.L, sug- and performance. Best to worst in each row la- gesting that R is more in-distribution. Error bars indicate one standard deviation.
beled green orange. red.
Consider Fig. 3a, where NLL shows smooth improvement with correct slope at 1B on MATH500 while Acc. in Fig. is noisy and sloping the wrong direction.
(1.b) Not All NLL are Equal: Distributional Alignment. Furthermore, find that the quality of the NLL signal hinges on the gold label Y*. We define gold label as the string used for NLL on our proxy model. We observe that it should be closer to the pre-training distribution p(yrlæ; v22r) 7p where x is the input, and T the decoded token. Following Arora et al. (2021), Gonen et al. (2023), measure how in-distribution (ID) Y* is via NLL, - log(p(Y*))) The more out-of-distribution (OOD), the more lacking in signal the proxy model becomes (Fig. 3). Benchmarks with an ID Y (lower NLL) show smooth and predictable progress smaller model scale (IB) (Fig. 3a). Contrarily, benchmarks with OOD Y (higher NLL) is at least as noisy as the target metric Acc. (Fig. 3b).
We also observe this distributional misalignment in ScalingBench (SeB; Xiao et al. (2024); Team (2025)). ScB proposes we set the reasoning trace Rd (Wei al., 2022b) and the final answer label A of a frontier model の as Y ScB's s gold labels include formatting artifacts like "(n", "Final Answer:", and "I hope is correct." that rarely appear in pre-training data, making them OOD, hurting proxy performance (Tab. 1). The proxy evaluation protocol is detailed in $ 4.1 (ii).
(2) Target Task Alignment of Gold Label Y and at the Token Level. Secondly, we hypothesize that the evaluation scheme should be task aligned. Here, target task refers to, e.g., correctly solving math problems for GSMSK (Cobbe et al., 2021), and MATH500 (Hendrycks et al., 2021b). Ensuring that Y is aligned with the target task is necessary to fulfilling performance at large scale. While we could achieve maximal ID by setting the greedy decoded tokens of 7 - sr, v2,)) Y*, this would provide no signal, as it would fail to be task aligned.
ultimate goal of proxying task
We further observe that standard NLL on Y does not distinguish between tokens that are important for task alignment, and others that are less task critical. Consider, Fig. where frontier model x tokens. For example, newlines and
decoding produces both task critical and formatting/creatte numbering are not essential, while steps like "sum modulo 9" are crucial. To further achieve task alignment, tokens should not be arbitrarily given equal weight as is in NLL.
3.2 RBRIDGE: IMPROVING EVALUATION OBJECTIVE ALIGNMENT AND TASK ALIGNMENT
Reasoning Trace R as y We show that using only the reasoning trace R frontier model T as Y satisfies both being (1) ID and (2) task alignment. (1) We hypothesize that R is more distributionally aligned (ID) with the pre-training distribution comprised predominantly a collec- tion of continuous long texts (Penedo et al., 2024; Kydlfček et al., 2025). We verify this empirically in Fig. 4, where we visualize average NLL across 250 to 3750B trained tokens on a 1B model, on the provided Y* by the benchmark dataset D against the frontier model の generated Rd. We observe an average NLL decline of 74.7% when using Rd across five reasoning benchmarks, pro- viding supportive evidence that R is more ID. (2) Rd is well aligned with the target task R
4

---

Published as a conference paper at ICLR 2026
the reasoning trace that leads to the correct final answer for Acc./p@k. Intuitively, evaluating how well the model x² reasons towards the final answer a good ID proxy for achieving the target Acc//p@k. Empirically, using only R achieves the best relationship MATH500 which able to replicate as the exact SeB Y* gold labels have been released for MATH500 (Tab. 1).
Weighted NLL R Tokens for Further Task Alignment. RBRIDGE takes final step for further task alignment by weighting each token by its level of task-alignment. We hypothesize that frontier model token probability p (token;) T provide automatic task-alignment weights:
RBRIDGE NLL((oken:)
-log(p" (token)))
(letter)
token:
standard NLL
letter
Automatic tokenizer-agnostic task-alignment weight
Our method weighs each token i's NLL by the frontier model's confidence in that token p (token:). To handle different tokenizers, we average letter-level probabilities within each token. Finally, we apply MinMax normalization (Witten & Frank, 2002) on the weight factor in Eq. (1) amplify the effect. Consider Fig. 1 for an intuitive visualization of Eq. (1) with the MinMax normalized form unpacked in full. The pseudocode and prompt available at Appendix B.
EMPIRICAL STUDY
Our experiments are organized in three stages: (i) first, we show that RBRIDGE can be used to rank datasets from proxy scale of <100M target scale 1.2B, (ii) next, we examine the RBRIDGF-target relationship across different amounts of training data at IB to 32B scale, and (iii) finally, demonstrate that the relationship derived in (ii) can be effectively transferred to a different dataset, allowing us to predict and rank large-scale datasets' reasoning performance at fraction of the cost.
4.1 EXPERIMENTAL PROTOCOL
All experimental settings are set a priori, and all methods are evaluated the same manner. Detailed experimental details are available in Appendix C.
(i) Dataset Ranking at <100M - 1.2B. We evaluate whether RBRIDGE scores from proxy mod-
els can effectively rank pre-training datasets for target model performance. Following DataDecide's protocol (Magnusson et al., 2025), we rank 25 datasets using proxy models and assess alignment with target model rankings. We measure effectiveness using Decision Accuracy (DAcc.; fraction of correctly ordered dataset pairs; Magnusson et al. (2025)) and Kendall's Tau correlation. Compute cost measured FLOPs 6N D, where N model size, and D is dataset size, following Kaplan et al. (2020).
Setup. Proxy models range n E (37MM, 97.9M), with 1.2B target model. As discussed in $ noisy benchmarks such as MATH500 and MMLU Pro are excluded at this scale. Instead, we use ARC-C (Clark et al., 2018) and CQA (Talmor et al., 2019), which yield stable cloze-form (CF) accuracy (Gu et al., 2025) when averaged across three pre-training seeds (Magnusson et al., 2025). Reported results are the average over these two benchmarks.
Baselines. Alongside RBRIDGE, we compare five dataset-ranking metrics commonly used with proxy models: Correct Probability, Normalized Correct Probability, Total Probability, Margin, and CF Accuracy (Xie et al., 2023; Liu et al., 2025; Magnusson et al., 2025).
(ii) Proxy-target Relationship Across Different Amounts of Training Data at 1B 32B. We test whether proxy-scale metrics can predict changes in target-scale Acc. and p@k k across different training data sizes. Each data point corresponds a specific data size from the same source (Fig. 5). Following Che et al. (2018), Lee et al. (2020), we evaluate using 5-fold cross validation setup visualized in Fig. 5, reporting average train R² and test Mean Absolute Error (MAE; Chai & Draxler (2014)). Curve fitting selects the best function based train R² from our hypothesis space: linear, quadratic, exponential, and logarithmic. This hypothesis space was defined a priori to avoid overfitting. FL.OPs
the
5

---

the target and Published as a conference paper at ICLR 2026
16-
Test Data
Train Data
3750B 27500 #
32508,
55008
16
a
Test Data
Train Data
2750g
320
bog
37508
15
15
138: Accuracy
1.4
22508
1500
7508
30008
Accuracy
14-
7508
1750B
22509
133
25008
1.3
25008
12508
15006
20008
12508 50f0
12
12
10008
10000
11.
11
2508
500B
2500
5008
10
10
5.0
6.0
-1.16
-1.14
-1.12
-1.10
-1.08
-1.06
-1.04
18: Accuracy (%)
18: rBridge (negated)
(a) Target metric Acc. as the proxy metric at IB.
(b) RBRIDGE as the proxy metric at IB.
Figure 5: Example visualization from fold of proxy-target relationship study at 1B + 13B on MMLU Pro (STEM). Each data point represents equal trained tokens for the proxy and target model. Extended visualizations for all other benchmarks available in Appendix D.
Setup. We examine model scales of 1B - 13B and IB + 32B, pre-trained on 250B to 3750B tokens in 250B token intervals using the OLMo-Mix- 124 dataset (OLMo al., 2024). Evaluation spans six reasoning benchmarks: GSM8K, MATH500, ARC-C, MMLU Pro (STEM subset; Wang al. (2024)), CQA, and HumanEval (Chen al., 2021).
Baselines. We cover six alternative metrics that examine the relationship of small and large models given the same data source, and metrics that have shown non-emergent continuity at small scale. First, we include the most naïve approach, the target metric: Accuracy and Pass @ (Acc./p@) I; Ku- lal et al. (2019)). Second, we include intermediate supervised fine-tuning (iSFT; Snell et al. (2024)) which demonstrates that including SFT stage throughout the intermediate pre- pre-training checkpoints helps target metric (Acc./p@) 1) be used as signals. Third is Token Edit Distance (TED; Schaeffer al. (2023)) which argue that emergence occurs due to discontinuous metrics, and correspond- ingly proposes their continuous metric. Fourth is Model Probability of Correct Answer (MPCA) as demonstrated in Schaeffer et al. (2023), Snell et al. (2024) as a continuous metric. Last is SeB which visualizes the relationship between ScB and target metric using numerous smaller proxy mod- use our proposed improved version
els (similar the scaling law literature). As mentioned in $ of ScB, R as there were insufficient details to replicate ScB on all of our benchmarks.
(iii) Zero-shot Functional Relationship Transfer Across Dataset at 1B + 7B. Finally, ask: if we fit an empirical function on a single pre-training dataset D as in (ii), can this function transfer directly to different dataset D p'rr? Concretely, once we fit Acc//p@k- f(RBRIDGE) on D pre. can this learned function transfer zero-shot (i.e., with additional fitting) to Dirre where Pre # mime? ? If so, we could predict the performance (Acc./p@k) of D target scale using only the proxy model x?, reducing compute by factor of m/n, recalling that m, proxy sizes, respectively. This allows us to both estimate performance for any number of additional datasets and rank them given pre-training size Drr N+, simply by inputting the RBRIDGE score in (-) after training on Dee tokens from D' Drre.
Setup. We curate an additional pre-training dataset Djrc described in Appendix C. Then, we ex- amine how accurate such function as deseribed in (ii), zero-shot transfers from the OL 124 to our alternative dataset at IB + 7B at IT tokens. While larger-scale studies across more model sizes and pre-training datasets would be ideal, training an additional IB and 7B model for IT tokens required thousands of H100 hours, making further comparisons prohibitively expensive.
4.2 RESULTS
(i) Over 100x Dataset Ranking Compute Saving at <00MM + 1.2B, RBRIDGE significantly improves DAcc. given equivalent compute (Fig. 6a). For example, at the most compute-efficient point (3.7M model trained on 87.3M tokens), RBRIDGE achieves up to 27% higher DAce. than baseline metrics. This is remarkable as at this proxy compute level, CF Accuracy and Norm Correct

---

Published as a conference paper at ICLR 2026
Proportion of Target Compute
30
Alterative Pareto Pronterr
rBrige
80
100.2x
10th
100.2x
Decizion Accuracy
160.6x
201.2x
Nor Coreett
Nargin
10
50
Bendom Decison
Tita
Correct Prob CP Accunacy
45-
10a7
10M
FLOPs
Decision Accuracy (%)
(a) Decision Accuracy results across 3.7 -97.9M proxy (b) RBRIDGE saves FLOPs by a factor of x00.2x to model size, and across intermediary checkpoints.
733.4x.
Figure 6: RBRIDGE improves the pareto frontier in pre-training dataset ranking for 1.2B target model size. Values are averages aggregated across ARC-C and CQA. For intuitive reference, the two most compute efficient points in RBRIDGE'S pareto frontier is (1) 3.7M model size trained on 87.3M tokens, and (2) 6M model size trained on 81.6M tokens.
Prob displays random decision levels (~ 50%). To achieve the same level of DAcc., RBRIDGE uses 100.2x to 733.4x less FLOPs (Fig. 6b). Kendall's Tau results are reported in Appendix D. As the two evaluation metric are intimately related, the results are highly similar.
(ii) Consistently Strongest Relationship at 1B-+13B, 32B. In IB -1BB RBRIDGE achieves the best R²2 and MAE in 10/12 cases, and ranks near the top in the remaining two (Tab. 2). Similar performance occurs in 1BB+1B++SB where include single epoch SFT stage at target scale, and 1B-B2B. For IB 1B-+133 13B+SFFF and IB IB-32B, granular benchmark- results appear in Appendix D. Aligned with the literature emergence, discontinuous metrics (Acc./p@i 1, iSFT) demonstrated worst performance. The remaining continuous metrics performed better, with R BRIDDG''S state-of- the-art performance achieving train R² of 0.826 0.874 and test MAE of 1.304 1.481.
Additionally, even as we increase the proxy model size by 7x, , 13x, the target metric fails to out- perform RBRIDGE (Fig. 7a). Notably, RBRIDGE'S and best alternative box-and-whiskert box does not overlap, suggesting a meaningful improvement. To understand where these gains originate, we ablate the components RBRIDGE in Fig. 7b, showing that each part of the RBRIDGE NLL (Eq. 1) plays important role to its effectiveness across all three experimental settings. Note that we have an ablation study on going from NLL to ScB R in $ 3.2, therefore, skip this part here.
(iii) Successful Zero-shot Functional Relationship Transfer at IB-+7B. We demonstrate that functions fitted on dataset pre with RBRIDGE (from (ii)) can zero-shot transfer to alternative pre- training dataset pre reducing additional experimental compute cost by factor m (Tab. 3). In our experiment setting of 1B BB+7BB this yields 7x compute reduction since only proxy model of the target size is required. The transferred function achieves low MAE of 0.043 1.417 across most benchmarks (one outlier at 9.716), outperforming R2 For dataset ranking using predicted aceu- racy, RBRIDGE achieves perfect 5/5 performance versus R 4/5. This demonstrates that improved function fitting from (ii) translates to superior zero-shot transfer performance across datasets.
DISCUSSION
Additional Related Work. Zhang al. (2024) uses proxy benchmarks with small proxy models to predict pre-training performance of larger target models. However, their approach suffers from several limitations. First, computationally expensive, requiring global search over 42 bench- marks and 34 models. Second, the required size of the search space (number of benchmarks and models) lacks principled justification. Third, using fundamentally different test distributions intro- duces suboptimal noise,
empirically demonstrate in Appendix E.
Xie al. (2023) and Liu et al. (2025) leverage small proxy models (1 to 280M parameters) to optimize pre-training data mixture weights for larger target models (~7B), operating under the

---

Published as a conference paper at ICLR 2026
Benchmark
Method:
Acc/pp@1
iSFT
TED
MPCA
NLL
R
RBRIDGE
1B + 13B (Pre-to-Pre)
Train R
0.401
0.385
0.558
0.116
0.853
0.947
0.944
GSM8K
Test MAR
5.189
4.848
5.961
84.006
3.210
1.837
1.751
Train R²
0.127
0.076
0.213
0.025
0.861
0.864
0.890
MATH500
Test MAE
1.276
1,008
1.134
3,943
0.593
0.526
0,525
Train R
0.200
0.166
0.547
0.319
0.166
0.950
0.969
ARC-C
Test MAE
7.287
8.750
6.197
25.970
7.058
1.546
1.246
MMLU Pro
Train R²
0.167
0.199
0.200
0.207
0.897
0.897
(STEM)
Test MAE
1.624
1.572
1.495
12.582
0.575
0.574
Train R²
0.666
0.534
0.677
0.323
0,139
0.890
0.890
CQA
Test MAE
3.989
5.885
3.010
1697.096
5,483
2.203
2.182
Train R
0.260
0.057
0.179
0.685
0.655
0.652
HumanEval
Test MAE
2.889
2.389
3.341
2.111
2.041
2.025
Average Train R (t)
0.304
0.290
0.375
0.194
0.485
0.867
0.874
Average Test MAE (L)
3.709
5,123
3.377
302.642
5.173
1.455
1.384
1B
13B
SET (Pre-to-Post)
Average Train R (t)
0.329
0,302 5,251
0,517
0.257 27.062
0.413
0.820
0.846
Average Test MAE (L)
4.375
4.236
3.932
1.549
1.304
1B - 32B (Pre-to-Pre)
Average Train (t) Average Test MAE (L)
0.312
0,349
0,352
0.205
0.488
0.820
0.826
19.785
5,165
3.546
21.833
3.276
1.540
1.481
Table 2:
IB-+1B and IB IB-+32 performance across mathematics, science, engineering, common- sense, and coding benchmarks. Train fitting and test done using 5-fold cross validation ($ 4.1). Best value across methods is bolded, and second best is underlined.
rBridge
+Nommalization
1.55
10
0.87
(T)
0.86
Test MAE
x
0.85
-1.45
3 x 100
0.04
2 x 10
0.03
1.35
1.30
Acc.ipp1
Acc./pe1
Acc.ip81 138
rBridge
(Pre-to-Pre)
(a) BRIDGE outperforms proxy models 7 13x larger (b) Ablation shows that RBRIDGE NLL and normaliza- using the target metric (Acc./p@) 1).
tion results in consistent improvement.
Figure Additional experimental results demonstrate clear advantages of our proposed method.
sumption that small-scale performance meaningfully correlates with large-scale performance. Both works rely standard NI.L or perplexity metrics, which we demonstrate to be suboptimal in $ 3.1. Furthermore, their experiments are predominantly on non-emergent benchmarks (e.g., TriviaQA and HellaSwag), whereas our work specifically targets reasoning benchmarks that exhibit emergent be- havior. Lastly, their approaches are specialized for optimizing pre- pre-training data mixtures, whereas method addresses the more general problem of predicting large model performance with small proxy models, enabling broader applications.
Moreover, readers view Prudéncio al. (2024), Schellaert et al. (2025), Kipnis et al. (2025), and Pacchiardi et al. (2025) for some past works on LLM performance prediction, and evaluation. We acknowledge that alternative branches of the literature have studied predicting L.IM performance.
8

---

Published as a conference paper at ICLR 2026
1B 7B Zero-shot Functional Relationship Transfer from Paw to Pire
Method
Benchmark:
GSM8K
MATHS00
ARC-C
MMLU Pro
CQA
Averuge
Ground Truth
Acc. (%) of Pre
10.538
2,800
56,911
10.225
60.442
28.183
Acc. (%) of Pire
8.264
3,600
51.536
9.578
44.554
23,506
Acc. (%) Prediction D
9.886
3.116
52.558
10.649
$7,479
26,738
R
Rank(D pmu Pirn w. Prediction (1)
4/5
MAE ())
1.622
0,484
1.022
1.071
12.926
3.425
Acc. (%) Prediction of Drc
8.220
3.044
52.254
8.161
54.269
25.190
rBridge
Rank(P pres Prre) w.
(t)
MAE (1)
0.043
0.556
0.718
1.417
9.716
2.490
Table 3: RBRIDGE demonstrates strong zero-shot functional relationship transfer across datasets. Ground Truth rows are the Acc. performance and ranking (rank 1, rank 2) that the prediction is aiming attain. RBRIDGE achieves 0 1.5 (%) error-rate excluding one outlier, and perfectly ranks the datasets on all five benchmarks. Average values are aggregated across the row.
Minimal Compute Overhead. While RBRIDGE introduces some additional costs, these are mi- nor. First, is generating the gold reasoning trace R via the frontier model. However, this represents a small one-time cost of under $10 per benchmark. To help the research community we plan to open-sourced our dataset, allowing future researchers skip this cost for benchmarks we examine. Second, the automatic weighting mechanism (Fig. 1) incurs only few seconds of CPU runtime per benchmark. Both overheads are negligible compared the computational savings our method provides.
RBRIDGE is a Better Predictor of Large Scale Pre-training Performance on Reasoning. While we would expect the same metric to yield the greatest relationship and predictive power, our paper demonstrates that this is not the case. As visualized in Fig. 7a even as we scale the proxy model by 7x, 13x, it fails to achieve the performance levels of RRRIDGE. Our work empirically demonstrates that improved alignment with the (1) pre-training evaluation objective, and the (2) task is key to successfully leveraging small models to proxy large model's reasoning performance. Our ablative studies clearly indicate that each component (1, 2) of RBRIDGE is valuable (Tab. I, Fig. 7b).
Significant Compute and Economic Cost Reduction. We observe definitive computational cost saving gains through RBRIDGE. In our first experiment (i), we demonstrate that through RBRIDGE proxy models that are 324.3x (proxy models 3.7
T200 the size of target) smaller can be strong proxies for dataset ranking. From a computational perspective, RBRIDGE achieve at least 100.2x compute savings against the best baseline in achieving the same ranking performance. In our second ex- periment (ii), we demonstrate that 13x and 32x smaller proxy models can be effectively used as proxies. We believe that RBRIDGE'S utility extrapolates much greater proxy-target model scale differences. is also important to note that, by extension, RBRIDGE has the potential to signif- icantly reduce environmental footprint. This has meaningful societal impact as foundation model development has consequential environmental costs (Winsta, 2025; Morrison et al., 2025).
Enabling Zero-shot Functional Relationship Transfer Across Datasets. To our knowledge, we are the first to show that the proxy-target function fitted on one pre-training dataset (as shown in experiment (ii)) can be successfully transferred to an alternative one, with no additional fitting, We observe that the improved relationship fitting of RBRIDGE in experiment (ii)'s Tab. 2 extends to improved prediction (MAE) and ranking performance in Tab. 3. This saves x compute as any additional pre-training dataset's performance can be reasonably approximated.
Practical Potential Application: Two-stage Dataset Optimization. Practitioners optimizing pre-training data navigate a vast candidate space of data sources and mixtures (Xie et al., 2023; Han et al., 2025; Liu et al., 2025). The experiments work for such dataset optimization that can be derived as a strong future work. [Stage 1] Given N candidate datasets, following the setting of experiment (i) offers a cost-effective way to filter out N k poor datasets e.g. <100M proxy scale, reducing the candidate space to k. With high decision accuracy of ~80% (Fig. 6). we can expect bad datasets to have been filtered out correctly. Using relatively easier reasoning benchmarks (e.g., ARC-C) suffices for filtering out weak datasets.
4 provide a practically applicable frame-

---

Published as a conference paper at ICLR 2026
This approach works because (1) strong candidates must also perform well on these tasks, and (2) their performance still correlates with more difficult ones (Fig. 12), even if they are not strong enough to serve accurate predictors for ranking the downstream reasoning task. [Stage 2] For the remaining k datasets, we train larger BBssclle proxies following (ii) and (iii) to accurately rank them by predicting performance at the target scale (e.g., 32B). Although one training
the target scale m is needed, it is rare for practitioners to work without baseline target model to improve upon.
Denoting the required proxy and target compute CJ and C. for proxies and C" for target, it can be assumed that these costs C' are directly proportional to model size (Kaplan et al., 2020). The required total compute under this framework is NCT + kC. + C* compared to the naive approach .C
cost NC Given that the cost reduction factor is NCf- cr and CP,C3 < C", we can see how cost savings improve the number of candidate datasets N enlarge. Considering that the candidate space N is very large in the real-world, this framework enables practitioners to optimize pre-training datasets fraction of the cost.
5.1 LIMITATION AND FUTURE DIRECTION
We discuss several limitations of RBRIDGE that present opportunities for future research. First, frontier models do not achieve perfect accuracy on reasoning tasks, potentially resulting imper- fect extracted reasoning trace R While we utilize all available R without filtering and demon- strate substantial performance and efficiency gains. Our preliminary experiments showed minimal improvements from post-hoc filtering of incorrect generations. Future work could investigate more sophisticated quality assurance mechanisms, such as ensemble methods leveraging multiple frontier models.
Second, frontier models occasionally fail to produce outputs in the required format for reasoning trace R extraction. Our current approach provides additional generation attempt before cluding the sample from training. This represents a limitation that future research could address through more robust prompt engineering, ensembling multiple frontier models.
Finally, while we present potential framework for further practical application in $ 5, the efficient and effective implementation of this system remains an open challenge. Future work further explore how to best leverage the methodological contribution of RBRIDGE ($ 3) and experimental robustness ($ on more complex frameworks.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our work conforms to the ICLR Code of Ethics. We report minor use LLMs for polishing writing. All LLM-generated content has been reviewed and verified by the authors, who take full responsibility for the accuracy and integrity of the work presented.
7 REPRODUCIBILITY STATEMENT
Our method RBRIDGE is fully reproducible using the information provided in this paper. The ap- proach follows straightforward process: applying specific prompt to frontier model, then com- puting our proposed negative log-likelihodd as presented in Eq. (1) and illustrated in Fig. 1. We provide the exact prompt in Appendix along with pseudocode (Alg. for implementation. To further support reproducibility and benefit the research community, we open-sourced our dataset.
REFERENCES
Ehtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Co- jocaru, Mérouane Debbah, Étienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, et al. The falcon series of open language models. arXiv preprint arXiv:2311. 2023.
Udit Arora, William Huang, and He He. Types of distr--dit texts and how to detect them. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 10687-10701, Online and Punta Cana, Dominican Republic, November 2021. Associa- tion for Computational Linguistics. doi: 10. 10.18653/v1/2011 eem.llemaaiiiii 835. URL https: // //aclanthology orq/2021. mamlp-mmiin. 835/.
Peter F. Brown, Vincent J. Della Pietra, Peter V. deSouza, Jenifer C. Lai, and Robert L. Mercer. Class-based n-gram models of natural language. Computational Linguistics, 18(4):467 1992. URL https::t/// www.nnhhhoooeeooooooooooooccoooe
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33:1877-1901, 2020.
Tianfeng Chai and Roland R Draxler. Root mean square error (rmse) mean absolute error (mae)?- arguments against avoiding rmse the literature. Geoscientific model development, 7(3) 1250, 2014.
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. Recurrent neural networks for multivariate time series with missing values. Scientific reports, 8(1):6085, 2018.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. 2021.
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv: 1803. 2018.
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichrro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110. 14168, 2021.
Ben Cottier, Robi Rahman, Loredana Fattorini, Nestor Maslej, Tamay Besiroglu, and David Owen. The rising costs of training frontier ai models. arXiv preprint 2405.22000.20000 2024.
DeepSeek-AL, Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Cheng- gang Zhao, Chenggi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangho Hao, Guanting
11

---

Published as a conference paper at ICLR 2026
Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang. Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jiawei Wang, Jin Chen, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, Junxiao Song, Kai Dong Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang. Lei Xu, Leyi Xia, Liang Zhao, Litong Wang, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang. Peng Zhang, Qiancheng Wang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruisong Zhang. Ruizhe Pan, Runji Wang, Runxin Xu, Ruoyu Zhang, Ruyi Chen, S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shengfeng Ye, Shirong Ma. Shiyu Wang. Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Shut- ing Pan, T. Wang, Tao Yun, Tian Pei, Tianyu Sun, W. L. Xiao, Wangding Zeng. Wanjia Zhao, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wengin Yu, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xi- aokang Zhang, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xingkai Yu, Xinnan Song, Xinxia Shan, Xinyi Zhou, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, Y. K. Li, Y. Q. Wang, Y. X. Wei, Y. X. Zhu, Yang Zhang, Yanhong Xu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang. Yi Yu, Yi Zheng, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Ying Tang, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma. Yiyuan Liu, Yongqiang Guo, Yu Wu, Yuan Ou, Yuchen Zhu, Yuduan Wang, Yue Gong, Yuheng Zou. Yujia He, Yukun Zha, Yunfan Xiong, Yunxian Ma, Yuting Yan, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Z. F. Wu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhen Huang, Zhen Zhang. Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhipeng Xu, Zhiyu Wu, Zhongyu Zhang, Zhuoshu Li, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Ziyi Gao, and Zizheng Pan. Deepseek-v3 technical report, 2025. URL https: /atp:i////rvvv.//// org/ abs 2412.422222 19437.
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Wuinn Hon. Unified language model pre-training for natural language understand- ing and generation. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (eds.), Advances in Neural Information Processing Systems, volume 32. Cur- www.onggg..euuure.. neurips cttpcccr_pppppppeeeeeeeee 4d9aa333d495cccc ran Associates, Inc., 2019. URL orr.raaee....ccc.aacaaacaaaaaar.
Zhengxiao Du Aohan Zeng, Yuxiao Dong, and Jie Tang. Understanding emergent abili- ties of language models from the loss perspective. In A. Globerson, L. Mackey, D. Bel- grave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Advances in Neural In- formation Processing Systems, volume 37, pp. 53138-5311 531677 Curran Associates, Inc., 2024. URL https: htttps::/proceee http://poooeeeigg...eeeee neurips ce/ saaper_fiessppppett Paper Conference, pdf.
Hila Gonen, Srini Iyer, Terra Blevins. Noah A. Smith, and Luke Zettlemoyer. Demystifying prompts in language models via perplexity estimation. In The 2023 Conference on Empirical Methods in Natural Language Processing, 2023. URL https: /ppnnevview www.ewew..... nww. / forum? d= NPJ2nfA7ZC.
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravänkumar, Artem Ko- renev, Arthur Hinsvark. Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru. Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, Danny Wyatt, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Francisco Guzmán, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Govind That- tai, Graeme Nail, Gregoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Kore- vaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel Kloumann, Ishan Misra,
12

---

Published as a conference paper at ICLR 2026
Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Ma- hadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jong- soo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Karthik Prasad, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini, Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Kushal Lakhotia, Lauren Rantala-Yeary, Laurens van der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin, Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira, Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marein Kardas, Maria Tsimpoukelli, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike Lewis, Min Si, Mitesh Ku- mar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Nikolay Bashlykov, Nikolay Bogoy- chev, Niladri Chatterji, Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic. Peter Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ra- mon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Raileanu, Rohan Maheswari, Ro- hit Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabasappa. Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang, Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten Sootla. Stephane Collot, Suchin Gururangan, Sydney Borodinsky, Tamar Herman, Tara Fowler, Tarek Sheasha, Thomas Georgiou, Thomas Scialom, Tobias Speckbacher, Todor Mi- haylov, Tong Xiao, Ujjwal Karn, Vedanuj Goswami, Vibhor Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do. Vish Vogeti, Vítor Albiero, Vladan Petrovic, Weiwei Chu, Wenhan Xiong. Wenyin Fu, Whitney Meers, Xavier Martinet, Xiaodong Wang. Xiaofang Wang. Xiaoqing Ellen Tan, Xide Xia, Xinfeng Xie, Xuchao Jia, Xuewei Wang. Yaelle Gold- schlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen Song, Yuchen Zhang, Yue Li, Yuning Mao, Zacharie Delpierre Coudert, Zheng Yan, Zhengxing Chen, Zoe Papakipos, Aaditya Singh, Aayushi Srivastava, Abha Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alexei Baevski, Allie Feinstein, Amanda Kallet, Amit Sangani, Amos Teo, Anam Yunus, Andrei Lupu, Andres Alvarado, An- drew Caples, Andrew Gu, Andrew Ho, Andrew Poulton, Andrew Ryan, Ankit Ramchandani, An- nie Dong, Annie Franco, Anuj Goyal, Aparajita Saraf, Arkabandhu Chowdhury, Ashley Gabriel, Ashwin Bharambe, Assaf Eisenman, Azadeh Yazdan, Beau James, Ben Maurer, Benjamin Leon- hardi, Bernie Huang. Beth Loyd, Beto De Paola, Bhargavi Paranjape, Bing Liu, Bo Wu, Boyu Ni, Braden Hancock, Bram Wasti, Brandon Spence, Brani Stojkovic, Brian Gamido, Britt Mon- talvo, Carl Parker, Carly Burton, Catalina Mejia, Ce Liu, Changhan Wang, Changkyu Kim, Chao Zhou, Chester Hu, Ching-Hsiang Chu, Chris Cai, Chris Tindal, Christoph Feichtenhofer, Cynthia Gao, Damon Civin. Dana Beaty. Daniel Kreymer, Daniel Li, David Adkins, David Xu, Davide Testuggine, Delia David, Devi Parikh, Diana Liskovich, Didem Foss, Dingkang Wang, Due Le, Dustin Holland, Edward Dowling, Eissa Jamil, Elaine Montgomery, Eleonora Presani, Emily Hahn, Emily Wood, Erie-Tuan Le, Erik Brinkman, Esteban Arcaute, Evan Dunbar, Evan Smoth- ers, Fei Sun, Felix Kreuk, Feng Tian, Filippos Kokkinos, Firat Ozgenel, Francesco Caggioni, Frank Kanayet, Frank Seide, Gabriela Medina Florez, Gabriella Schwarz, Gada Badeer, Georgia Swee, Gil Halpern, Grant Herman, Grigory Sizov, Guangyi, Zhang, Guna Lakshminarayanan, Hakan Inan, Hamid Shojanazeri. Han Zou, Hannah Wang, Hanwen Zha, Haroun Habeeb, Harri- son Rudolph, Helen Suk, Henry Aspegren, Hunter Goldman, Hongyuan Zhan, Ibrahim Damlaj, Igor Molybog, Igor Tufanov, Ilias Leontiadis, Irina-Flens Veliche, Itai Gat, Jake Weissman, James Geboski, James Kohli, Janice Lam, Japhet Asher, -aan-stp Gaya, Jeff Mareus, Jeff Tang, Jen- nifer Chan, Jenny Zhen, Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang, Joe Cummings, Jon Carvill, Jon Shepard, Jonathan McPhie, Jonathan Torres, Josh Ginsburg, Jun- jie Wang. Kai Wu, Kam Hou U, Karan Saxena, Kartikay Khandelwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan, Kelly Michelena. Keqian Li, Kiran Jagadeesh, Kun Huang, Kunal Chawla, Kyle Huang, Lailin Chen, Lakshya Garg, Lavender A, Leandro Silva, Lee Bell, Lei Zhang, Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian Khabsa, Manav Avalani, Manish Bhatt, Martynas Mankus, Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev. Maxim Naumov, Maya Lathi, Meghan Keneally, Miao Liu, Michael L. Seltzer, Michal Valko, Michelle Restrepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan
13

---

Published as a conference paper at ICLR 2026
Singhal, Nick Egebo, Nicolas Usunier, Nikhil Mehta, Nikolay Pavlovich Laptev, Ning Dong, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji. Pedro Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Ro- driguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Rangaprabhu Parthasarathy, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang, Russ Howes, Ruty Rinott, Sachin Mehta, Sachin Siby, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Mahajan, Saurabh Verma, Seiji Yamamoto, Sharadh Ra- maswamy, Shaun Lindsay, Shaun Lindsay, Sheng Feng. Shenghao Lin, Shengxin Cindy Zha, Shishir Patil, Shiva Shankar, Shuqiang Zhang. Shuqiang Zhang. Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satter- field, Sudarshan Govindaprasad, Sumit Gupta, Summer Deng, Sungmin Cho, Sunny Virk, Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Koehler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews, Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Ku- mar, Vishal Mangla, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang, Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiao- jian Wu, Xiaolan Wang, Xilun Wu, Xinbo Gao, Yaniv Kleinman, Yanjun Chen, Ye Hu, Ye Jía, Ye Qi, Yenda Li, Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang. Yu Zhao, Yuchen Hao, Yundi Qian, Yunlu Li, Yuzi He, Zach Rait, Zachary Viti,,, Zef Rosnbrick. Zhao- duo Wen, Zhenyu Yang, Zhiwei Zhao, and Zhiyu Ma. The Ilama 3 herd of models, 2024. URL https:: pettps://areet /ttp//aavvvv////// alss / 2407 11407.27888 21783.
Yuling Gu, Oyvind Tafjord, Bailey Kuehl, Dany Haddad, Jesse Dodge, and Hannaneh Hajishirzi. OLMES: A standard for language model evaluations. In Luis Chiruzzo, Alan Ritter, and Lu Wang (eds.). Findings of the Association for Computational Linguistics: NAACL 2025, pp. 5005-5033, Albuquerque, New Mexico, April 2025. Association for Computational Linguistics. ISBN 979-8- 89176-195-7. doi: 10. 10.18653/v1/202 1.222..ninn naac1.282. URL https: / //tp//aaaaannooooooooooao org/2025. f find Findings-naace. .82/.
Sungjun Han, Juyoung Suk, Suyeong An, Hyungguk Kim, Kyuseok Kim, Wonsuk Yang. Seungtaek Choi, and Jamin Shin. Trillion 7b technical report. arXiv preprins arXiv:2504. 15431, 2025.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Ja- cob Steinhardt. Measuring massive multitask language understanding. In International Confer- ence on Learning Representations, 2021a. URL http:::s///pppeeeeeeeee net/ forum??mmmmmm iid= d7KB 3GmQ
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song. and Jacob Steinhardr. Measuring mathematical problem solving with the MATH dataset. In Thirty-fftth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021b. URL https: btttps://ppe.reeeeeee wwwp//peeeeeeeeeeee/pp/e. net/ wooumrrriiiooo Eorum?id-7Byyt 2mQa
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hen- nigan, Eric Noland, Katherine Millican, George den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack William Rae, and Lau- rent Sifre. An empirical analysis of compute-optinal large language model training. In Al- ice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (eds.), Advances in Neural Information Processing Systems, 2022a. URL https: /ttp:///pppeeeieeeeee net forumrirmddd iBBcRU10APR.
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Jardine Uesato, Jaehoon Bae, Jahnavi Gbp, Sven Gowal, Adrien Gane, Angeliki Lazaridou, Aedan Mck- inmon, Tayfun Roman, Oriol Vinyals, Laurent Sifre, Aäron van den Oord, Erich Elsen, Ryan Clark, Tom Ring, and Hroi Hrorbjartsson. Training compute-optimal large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 30433-30448, 2022b. URL httpss//pproceeddigg.euuuuuueuus cc http:/eeerieeeeeeeeeee//ee/ file/ 0111666644455522333354444444--p0
14

---

Published as a conference paper at ICLR 2026
Shengding Hu, Yuge Tu, Xu Han, Ganqu Cui, Chaoqun He, Weilin Zhao, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Xinrong Zhang, Zhen Leng Thai, Chongyi Wang, Yuan Yao, Chenyang Zhao, Jie Zhou, Jie Cai, Zhongwu Zhai, Ning Ding, Chao Jia, Guoyang Zeng, da- hai li, Zhiyuan Liu, and Maosong Sun. MiniCPM: Unveiling the potential of small language models with scalable training strategies. In Fürst Conference on Language Modeling, 2024. URL https: opwnreviewwwwweeeeeeeeeeee net / forum? id- 3x2221=
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Regina Barzilay and Min- Yen Kan (eds.), Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 1601-1611, Vancouver, Canada, July 2017. Association for Com- putational Linguistics. doi: 10. 11185//1//P11-11444 P17-1147/.
URL https:tpp//// hatps///aaaatthhooooooooooo .
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.0836/ 2020.
Apoorv Khandelwal, Tian Yun, Nihal V Nayak, Jack Merullo, Stephen H Bach, Chen Sun, and Ellie Pavlick. 100 k or 100 days: Trade-off's when pre-training with academic resources. arXiv preprint arXiv:2410.23261, 2024.
Alex Kipnis. Konstantinos Voudouris, Luca M. Schulze Buschoff, and Eric Schulz. metabench sparse benchmark of reasoning and knowledge large language models. The Thirteenth International Conference Learning Representations, 2025. URL https: // /pppnreviee..e. net
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang. Spoc: Search-based pseudocode to code. In H. Wallach, H. Larochelle, A. Beygelzimer, d'Alché-Buc, E. Fox, and R. Garnett (eds.), Ad- vances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://prooceeddngg.. neurips. cc pdf.
Hynek Kydlíček, Guilherme Penedo, and Leandro von Werra. Finepdfs. 2025.
https:/ //
huggingface, httpgiingaacccc/daaa.ooeo scoodatasets segeet//HHuggggg wtt.aaaaaaaaaeeeeeeeee
Jinhyuk Lee, Wonjin Yoon. Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jae- woo Kang. Biobert: pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4): 221111244-444 2020.
Qian Liu, Xiaosen Zheng, Niklas Muennighoff, Guangtao Zeng, Longxu Dou, Tianyu Pang, Jing Jiang, and Min Lin. Regmix: Data mixture as regression for language model pre-training. In The Thirteenth International Conference on Learning Representations, 2025. URL https:
oppereevie...ne wwwp:/ntte..onmmoeeeoooo 1d:53 j0OUxq7i.
Ian Magnusson, Nguyen Tai, Ben Bogin, David Heineman, Jena D. Hwang. Luca Soldaini, Akshita Bhagia, Jiacheng Liu, Dirk Groeneveld, Oyvind Tafjord, Noah A. Smith, Pang Wei Koh, and Jesse Dodge. Datadecide: How to predict best pretraining data with small experiments. In Forty-second International Conference on Machine Learning, 2025. URL https: http//ppeeeeeeeeeeeeeeeeee net// forum? forum?id-p9Y1@PP BIE.
Jacob Morrison, Clara Na, Jared Fernandez, Tim Dettmers, Emma Strubell, and Jesse Dodge. Holis- tically evaluating the environmental impact of creating language models. In The Thirteenth In- ternational Conference on Learning Representations, 2025. URL https: /pppnreview. net/forund (twoooommmiii0000x000000
Team OLMo, Pete Walsh. Luca Soldaini, Dirk Groeneveld, Kyle Lo. Shane Arora, Akshita Bhagia, Yuling Gu Shengyi Huang, Matt Jordan, et al. 2 olmo 2 furious. arXiv preprint arXiv:2501. 20060666000006, 2024.
15

---

Published as a conference paper at ICLR 2026
Lorenzo Pacchiardi, Konstantinos Voudouris, Ben Slater, Fernando Martinez-Plumed, Jose Hemandez--Oalle Lexin Zhou, and Wout Schellaert. PredictaBoard: Benchmarking LLM score predictabilityy. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pile- hvar (eds.), Findings of the Association for Computational Linguisties: ACI. 2025, pp. 15245- 15266, Vienna, Austria, July 2025. Association for Computational Linguistics. ISBN 979-8- 89176-256-5. doi: 10. 18252//1/20255 win.iiignnnnnlln1.. URL attpss//aclaatthloggr org/2025. findings acl. 790/.
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf. The fineweb datasets: Decant- ing the web for the finest text data at scale. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, Tomezak, and C. Zhang (eds.), Advances Neural Informa- tion Processing Systems, volume 37, pp. 308 30811-30849, Curran Associates, Inc., 2024. URL https: http///ppooongaae/eeeeeee/ces.ee neurips 370df50ccfdd Bf8f9c2d915555 bda - Datare-tttsstsssssaeeessssssssse Bat_s__annd_nnharreeee Benchmarks. Track.pds
Ricardo B. C. Prudêncio, Ana C. Lorena, Telmo Silva-Filho, Patricia Drapal, and Maria Gabriela Valeriano. Assessor models for explaining instance hardness in classification problems. In 2024 International Joint Conference Neural Networks (IJCNN), pp. 1-8, 2024. doi: 10.1109/
UJCNN60899.202 2004999..222.111 10651521.
Alec Radford, Karthik Narasimhan, Tim Salimans, and llya Sutskever. Improving language under- standing by generative pre-training. 2018.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and llya Sutskever. Language models are unsupervised multitask learners. OpenAI, 2019.
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo. Are emergent abilities of large language models a mirage?
In Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Information Pro- cessing Systems, volume 36, pp. 55565-55581. Curran Associates, Inc., 2023. URL fttps://proceedie neurigs...uu....... httpppperr.fllss/pppr///22
Wout Schellaert, Fernando Martinez-Plumed, and José Hernández-Orallo. Analysing the pre- dictability of language model performance. ACM Trans. Intell. Syst. Technol. 16(2), Febru- ary 2025, ISSN 2157-6904. doi: 10.1145/3706118. URL https:/ /tl:://doo. /tw../oiiiio/// wwt.dcioooo...10/////111 3706118.
Charlie Victor Snell, Eric Wallace, Dan Klein, and Sergey Levine. Predicting emergent capa- bilities by finetuning. In First Conference on Language Modeling, 2024. URL https: / //openzeview.co wwwpee/etmidddwaa/te.......
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. CommonsenseQA:: A ques- tion answering challenge targeting commonsense knowledge. In Jill Burstein, Christy Doran, and Thamar Solorio (eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume I (Long and Short Papers), pp. 4149-4158, Minneapolis, Minnesota, June 2019. Association for Com- putational Linguistics. doi: 10. 11..8663/1//11111422 URL https:
N19-1421/.
MiniCPM Team. Minicpm4: Ultra-efficient 1lms on end devices. arXiv preprint arXiv:2506.07900 2025.
Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weim- ing Ren, Aaran Arulraj. Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue, and Wenhu Chen. MMLU-pro: A more robust and challenging multi- task language understanding benchmark. In The Thirty-iight Conference Neural Information Processing Systems Datasets and Benchmarks Track, 2024. URL https: /ppenreview. net. net/forumm? forum? d=yl 0DM6R2r3.
16

---

Published as a conference paper at ICLR 2026
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent abilities of large lan- guage models. Transactions on Machine learning Research, 2022a. ISSN 2835-8856. URL Survey Certification.
htttp:://ppenree
net/ www..enooorrneeeeeeeeeeeee seerumiddyyzkuu--ae
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. Chain-of tf--hhugtt prompting elicits reasoning in large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 24824-24837. Curran Associates, Inc., cc/ 2022b. URL https::/ppooeeddinssneeee Paper Conference pdf. pdf. file://25601115224ffffffffff...3
Jenis Winsta. The hidden costs of ai: A review of energy, e-waste, and inequality in model develop- ment. arXiv preprint 1rXiv:2507.0661, 2025.
lan H Witten and Eibe Frank. Data mining: practical machine learning tools and techniques with java implementations. Acm Sigmod Record, 31(1):66-77, 2002.
Chaojun Xiao, Jie Cai, Weilin Zhao, Guoyang Zeng, Biyuan Lin, Jie Zhou, Zhi Zheng, Xu Han, Zhiyuan Liu, and Maosong Sun. Densing law of 1ls. arXiv preprini arXiv:2412.04315. 2024.
Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy S Liang, Quoe V Le, Tengyu Ma, and Adams Wei Yu. Doremi: Optimizing data mixtures speeds up language model pretraining. Advances in Neural Information Processing Systems, 36:69798- 69818, 2023.
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. HellaSwag: Can machine really finish your sentence? Anna Korhonen, David Traum, and Lluís Mårquez (eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguisties, pp. 4791-4800 Florence, Italy, July 2019. Association for Computational Linguisties. doi: 18633/v1/11--1472 URL https: / aaccaanthhoo ogy. wttp///-1///1/////////
Bo-Wen Zhang, Yan Yan, Boxiang Yang, Yifei Xue, and Guang Liu. Predictable emergent abilities of lims: Proxy tasks are all you need. arXiv preprint 1rXiv:2412.01111, 2024.
17

---

Published as a conference paper at ICLR 2026
APPENDIX
A ADDITIONAL RESULTS ON NOISY SMALL SCALE MODELS
Refer to Fig. 8.
B RBRIDGE PSEUDOCODE
Pseudocode. The pseudocode is available in Alg. 1. We recommend viewing with Fig. I to build intuition. Recall that is an input from the benchmark dataset's validation test we want to evaluate. Recall that TP, x are the proxy, and frontier LMs, respectively. denotes index of token 7. denotes the token tokenized using x tokenizer. Exact prompt and extraction used in Line available in prompt.
Algorithm 1: RBRIDGE
Input: xp, n
/* Step 1: Extract reasoning trace and token-level confidence
from frontier model
R (P,p((R).. A + (x);
/* Step 2: Discard answer label, and compute normalized
RBRIDGE NLL
w + [::
for each token of tokenizing R do
W; Mean lettree; (letter) from p( RR)):
w.append(w:)
RBRIDGE - []:
for each token of tokenizing R do
NLL + -log (T{)
RBRIDGFFappend (NLL. mifiimin(w)) wc)
10 RBRIDGE +- Mean (BBRIDGE);
Return: RBRIDGE
Prompt. Here the prompt we use to generate R. We use greedy decoding. Brackets [] indi- cate dynamic insertions depending on the benchmark and question. We extract the value for key "reasoning" to attain R
Prompt to Frontier Model
System: You are helpful assistant that solves [task] problems.
User: [question] Respond ONLY with JSON object in this exact format: { ""reasoning": "your step by step reasoning", "final answer": "your final answer"
C FURTHER EXPERIMENTAL DETAILS
Common Evaluation Setting.
All evaluations done using 5-shot CoT (Wei et al., 2022b).
C.1 HARDWARE
We A100 80G, H100 and H200 nodes and numerous terabytes of disk storage for experiments. For pre- vre-trainnng we use 256 H100 GPUs with HBM3. For other hardware like CPU and RAM we commonly available ones, these hardware did not induce any bottlenecks.
18

---

Published as a conference paper at ICLR 2026
C.2 EXPERIMENT (1) ADDITIONAL DETAILS
All proxy models used is the default seed provided in Magnusson al. (2025). We do not multi-seed averaging for the proxy as this significantly increases compute cost. We exactly follow Magnusson al. (2025) on every other aspect of experiments, using their open-source assets where available. All intermediary checkpoints are as available from Magnusson et al. (2025).
C.3 EXPERIMENT (II) ADDITIONAL. DRTAILS
Additional Baseline Details. iSFT results are unavailable for MMLU Pro (STEM) and Hu- manEval as these benchmarks do not provide an SFT set.
Frontier Model's Ro. Following Team (2025), use GPT 4o generate Ro. We tested Claude 3.5 Sonnet and Cimini 2.5 Pro and found no meaningful performance differ- ence. We have not tested this method on long CoT models.
Pre-training and Post-training Details. Unless stated otherwise, for pre-training, we fully follow OLMo 2 (OLMo et al., 2024), and use their checkpoints where available. For SFT post-training we use the settings in Tab. 4.
Table 4: Hyperparameters for SFT
Hyperparameter
Value
Epoch
1
Learning Rate
1 x 10-5
Warmup Ratio
0.1
Batch Size
64
C.4 EXPERIMENT (II) ADDITIONAL DETAILS
Alternative Pre-training Dataset D'. For the alternative pre-training dataset D' described in experiment (iii), closely follow the setup described in Han al. (2025), but exclu- sively use publicl--vvilable datasets. The training data follows an 8.5:1:0.5 ratio of En- glishmmuttinggulmmaatccodde where the English portion consists of DCLM and FineWeb-Eda in equal proportions, and the multilingual portion comprises Korean, Chinese, and Japanese dumps of FineWeh and DCLM pipeline-processed Common Crawl filtered for Korean.
Benchmark Choice. We use the same benchmarks presented in (ii), excluding HumanEval, as our extraction method on the alternative dataset D' achieved 0% p@) 1.
D ADDITIONAL EXPERIMENTAL RESULTS
Refer Fig. 9 and 10 for extended curve fitting visualization examples. Refer Tab. 5 for all IB + 13B (Pre-to-Post) benchmark results. MMLU Pro (STEM) and HumanEval are not included as they do not have designated SFT set. Refer to Tab. 6 for all - 32B (Pre-to-Pre) benchmark results. Refer to Fig. 11 for Kendall Tau results.
PROXY BENCHMARKS ARE UNRELIABLE
Refer Fig. 12.
19

---

Published as a conference paper at ICLR 2026
Benchmark
Method:
Acc./@@
iSFT
TED
MPCA
NLL
R
rBridge
Train R
0.363
0.385
0.565
0.129
0.868
0.951
0.956
GSM8K
Test MAE
5.807
5.536
7.054
79.761
3.302
1.715
1.616
Train R²
0.088
0.128
0.199
0.182
0.557
0.544
0.555
MATH500
Test MAE
1.178
0.888
0.805
0.849
0.692
0.609
0.605
Train R²
0.187
0.153
0.522
0.331
0.160
0.938
0.964
ARC-C
Test MAE
6.921
8.317
6.248
22.685
6.679
1.590
1.279
Train R²
0.677
0.543
0.783
0.385
0.068
0.845
0.910
CQA
Test MAE
3.593
6.264
2.837
4.952
5.055
2.283
1.715
Average Train R² (1)
0.329
0.302
0.517
0.257
0.413
0.820
0.846
Average Test MAE (1)
4.375
5.251
4.236
27.062
3.932
1.549
1.304
Table Detailed results of IB 13B+SFT (Pre-to-Post). Train fitting and test is done using 5-fold cross validation ($ 4.1). Best value across methods is bolded, and second best is underlined.
Benchmark
Method:
Acc./p@1
iSFT
TED
MPCA
NLL
R
rBridge
Train R
0.281
0.398
0.706
0.076
0.814
0.972
0.972
GSM8K
Test MAE
7.755
7.299
6.505
107.493
6.935
1.676
1.601
Train R²
0.193
0.196
0.166
0.019
0.823
0.832
0.834
MATH500
Test MAE
98.093
1.869
1.985
6.907
0.815
0.739
0.718
Train R²
0.206
0.169
0.582
0.346
0.144
0.926
0.949
ARC-C
Test MAE
4.734
5.976
4.491
7.588
4.985
1.560
1.384
MMLU Pro
Train R²
0.057
0.031
0.210
0.344
0.756
0.756
(STEM)
Test MAE
2.075
1.930
1.864
2.133
1.326
1.326
Train R²
0.624
0.634
0.528
0.332
0.046
0.765
0.766
CQA
Test MAE
4.105
5.516
3.630
4.199
3.664
2.382
2.384
Train R²
0.514
0.101
0.245
0.759
0.666
0.679
HumanEval
Test MAE
1.946
2.733
2.947
1,424
1.555
1.474
Average Train R (?)
0.312
0.349
0.352
0.205
0,488
0.820
0.826
Average Test MAE (L)
19.785
5.165
3.546
21.833
3.276
1.540
1.481
Table 6: Detailed results of IB + 32B (Pre-to-Pre). Train fitting and test is done using 5-fold cross validation 4.1). Best value across methods is bolded, and second best is underlined.
20

---

Published as a conference paper at ICLR 2026
F ATTRIBUTION
We attribute the neural network icon in Fig. as taken from Freepik (flaticon.com). Their guideline indicates that it can be used with attribution.
21

---

Published as a conference paper at ICLR 2026
GSMBK SSHOT Accuracy (19
20
0.184
GSMBK SSHOT (%)
138: 0.948
78: 0.910
1.5
30-
20
20
2000 2500 3000
500
1000
1500
2500
3500
Prestrained Tokens (B)
Pre-trained Tokens (B)
70
1B: R 0.347
13B: R = 0.753
78: 0.672
80
328: 0,688
ARC-C Accuracy (%)
60-
Accuracy
50-
60-
40
500
1000
1500
2000
2500
3000
35.00
500
1000
1500
2000
2500
3000
3500
Pre-trained Tokens (B)
Pre-trained Tokens (B)
16
18: R
0.074
20
138: R
0.854
78:
0.870
32B:
0.431
MMLU Accuracy
16-
10-
MMLU
14-
12-
10-
500
1000
1500
2000
2500
3000
3500
500
1000
1500
2000
2500
3000
3500
Pre-trained Tokens (B)
Pre-trained Tokens (B)
70
18: 0.553
75
138: R 0.751
60-
78: R 0.495
328: R² 0.530
CRA Accuracy (%)
50-
65
60
30-
55-
20-
50
500
1000
1500
2000
2500
3000
3500
500
1000
1500
2000
2500
3000
3500
Pre-trained Tokens (B)
Pre-trained Takens (B)
18: R² 0.393
138: R² 0.660
7B: R 0.826
328: R 0.566
HumanEval Pass@1
10-
16
14.
HumanEval
12:
20.
8-
500
1000
1500
2000
2500
3000
3500
500
1000
1500
2000
2500
3000
3500
Pre-trained Tokens
Pre-trained Tokens (B)
(a) Pre-training progress on IB 7B model
(b) Pre-training progress on 13B and 32B model
Figure 8: Given the same data source, smaller models exhibit significantly more noise and occasion- ally provide the wrong direction, making challenging to use smaller models to proxy larger model performance. R² values derived from linear curve fitting. MMLU corresponds to MMLU Pro (STEM).
22

---

Published as a conference paper at ICLR 2026
70.0
Train Data
37508
70
Train Data
27508
# 3500B
96
Test Data
35400
65.0
1 12508
22508
Accuracy (%)
65
22500
Accurac
62.5
15008
10008
60
10008
1490
15008
25000
30008 #
57.5
7508 5008
55
508808
52.5
50.0
50
2508
Accuracy (%)
-1.00
rBridge (negated)
-0.94
-0.92
-0.90
(a) Target metric Acc. as the proxy metric at IB CQA.
(b) RBRIDGE as the proxy metric at IB on CQA.
27508.08 22508
37500
27508
325@8508
138: Accuracy (%)
70-
32508
20008
1.008
25008
35008
x
30008
70
Train Data
Test
12508
1500e 20008
22508
25008
3000B
35008
60-
10008
Accuracy
60-
JNPB
55
50
5008
50-
5006
45-
Test Data
2508
2500
22
24
18:
Accuracy (%)
25
26
27
-0.88
-0.86
18: rBridge (negated)
-0.84
-0.82
-0.80
-0.78
(c) Target metric Acc. as the proxy metric on ARC-C.
RBRIDGE as the proxy metric 1B on ARC-C.
3750B
37506
3500B
*
Train Data
Train Data Test Data
3500B
M
138: Accuracy (%)
27508
32508
22508
27508
138: Accuracy
20008
25008
15f00
12500
12508
Bg
2000gees
2500p
30000
7508
10008,
2508
2508
10008
500g
Accuracy
rBridge (negated)
(e) Target metric Acc. as the proxy metric at IB on MATH500.
(f) R RBRIDGE as the proxy metric at 1B on MATH500.
37508
37508
138: Accuracy (%)
*
Test Data
Train Data
27508
32508
30-
86
Train Data
Test Data
27508
32508
27:00
30008
Accuracy
25
22500
30008
38008
17506
20
2000B
20-
1000H
750B
25008
10006
15008
15-
10000
7358-
12509
18000
25008
10
2508
5008
10.
2508
2.0
255
1B: Accuracy (%)
3.0
3.5
4.0
-0.76
-0.74
-0.72
18: rBridge (negated)
-0.70
-0.68
-0.6
-0.64
-0.62
(g) Target metric Acc. the proxy metric IB (h) RBRIDGE as the proxy metric 1B on GSM8K. GSMSK.
Figure 9: Example visualization from fold of proxy-target relationship study at IB + 13B on CQA, ARC-C, MATH500, and GSM8K. Each data point represents equal trained tokens for the proxy and target model.
23

---

Published as a conference paper at ICLR 2026
Train Data
3750B
35008
18
Train Data
3750
# 000
Test
16-
136: Pazs03
14-
17500
24-
17500
20008
$230M
27508
12508
32108506
30008
30006
1500E
25g08
22508
25008
10008
10
7588008
2
50082
5
6
7
-1.02
-1.00
-0
0.96
-0.34
1B: Pass@1
18: rBridge (negated)
(a) Target metric Acc. the proxy metric IB.
(b) RBRIDGE as the proxy metric at IB.
Figure 10: Example visualization from fold of proxy-target relationship study IB + 13B HvmanEval. Each data point represents equal trained tokens for the proxy and target model.
Proportion of Target Compute
Absmaative Front
10
20ur2
Kendall Tau
920.2x
640.1x
ISrigee lours)
0.1-
Morgin
Norm Correct Prob
1014
0.0
Random Decision
Correct Prob
Tola Prob
0.1
CF Accuracy
(a) Kendal Tau results across 3.7 97.9M proxy model (b) RBRIDGE saves FLOPs by a factor of 200.8 to 920.2x.
size, and across intermediary checkpoints.
Figure 11: RBRIDGE improves the pareto frontier in pre-training dataset ranking for 1.2B target model size. Values are averages aggregated across ARC-C and CQA. The two most compute effi- cient points in RBRIDGE'S 's pareto frontier is (1) 3.7M model size trained on 87.3M tokens, and (2) 6M model size trained on 81.6M tokens.
1.00
Same Denchmers
Same Bendhmark DE lerend Benchmek
0.75
D fffeett Benchmark
0.50
0.6-
Pearson
2.44
-0.2
0.2
-0.50
ABCIC
BASY
mooi
coue
Hellstwag
Rise
0.0
ARCC
ABC DeST
Bust
cos
hap
Target Benchmark
Target Benchmark
Figure 12: Correlation and decision accuracy performance using the same benchmark vs. different benchmark 97.9M 1.2B. Error bars indicate standard deviation. On average, different benchmarks performs sub-optimally and are noisy.
24


Reviews:
[{"review_id": "5txiWy2vnW", "reviewer": "Reviewer_4B33", "summary": "This paper introduces a new method to use a small proxy model to predict the reasoning performance of LLM. It uncovers that existing methods don’t work well because they fail to align with the pre-training objective and the target task. To address the limitations, the proposed method uses frontier-model generated gold reasoning traces for better alignment. Experimental results demonstrate the effectiveness of the proposed method.", "questions": "How was the compute cost measured? Is it measured in a real system or just calculated by the model size? What are the system (hardware and software) settings if a real system was implemented? Using the proposed method, can a larger proxy model work better and how much better than a smaller proxy model?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "+ using frontier-model generated gold reasoning traces for better alignment\n+ significantly improved performance in terms of compute cost, proxy model size, and zero-shot transferring from one pre-trained dataset to another", "weaknesses": "- needs a frontier-model to generate gold reasoning traces, making the proposed method less useful\n- it is unclear how the small proxy models less than <100M work so well", "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": "o0vFSA5Kcp", "reviewer": "Reviewer_bXEE", "summary": "This paper introduces RBRIDGE, a method for predicting large language model reasoning performance using small proxy models (≤1B parameters) by aligning evaluation with both the pre-training objective and the target task. RBRIDGE leverages reasoning traces from frontier models as gold labels and introduces a weighted negative log-likelihood metric that emphasizes task-critical tokens, achieving strong correlations across six reasoning benchmarks and reducing dataset ranking costs by over 100×. It further demonstrates zero-shot transfer of predictive relationships across pre-training datasets, enabling efficient performance estimation at large scale.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The discussed problem is both intellectually engaging and practically important for advancing efficient LLM development.\n\n2. The proposed RBRIDGE metric shows strong performance, achieving high correlation with large-model reasoning while greatly reducing computational costs.", "weaknesses": "1. The paper shows small models struggle with noisy tasks but doesn't clearly explain how to identify which datasets are noisy—more analysis on dataset characteristics would help.\n\n2. The frontier model choice matters: models like R1 and GPT-4o differ in style and training data, which could affect reasoning trace quality and RBRIDGE’s consistency.\n\n3. RBRIDGE is tested on math and QA tasks, but performance may differ on code (e.g., LiveCodeBench) or subjective tasks (e.g., Arena-Hard), where reasoning patterns vary.\n\n4. Figure 5(b) illustrates RBRIDGE’s strong fit, but similar plots on other benchmarks would strengthen the empirical support.\n\n5. Minor typos need fixing, e.g., “is becomes” in Figure 1’s caption.\n\n6. The paper exceeds 9 pages, which violates the rule.", "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": "3QJSFpMshr", "reviewer": "Reviewer_v8sd", "summary": "This paper proposes to reliably estimate the reasoning capabilities of large LLMs using small proxy models. \nThe paper shows that, as the reasoning capability is an emergent property of large LLMs, the vanilla loss obtained from small models can be noisy.\nThere are two key limitations identified: (i) evaluation objective misalignment (e.g., NLL loss v.s. pass@K, and off-policy Y distribution ) (ii) task alignment and at the token-level (e.g., important tokens like branching/searching/backtracking should be taken into consideration)\n\nThe proposed rBridge tackles the limitations via (i) using a reasoning trace generated by frontier models as a proxy of Y and (ii) the loss is weighted according to the normalized log-probs of the frontier model, where the potential tokenization mismatch issue is tackled by using the character-level average.\n\nExperimental results show the proposed rBridge successfully predicts the model performance using much smaller proxy models (< 100M) for a 1.2B target model. The correlation stays strong when using the 1B model to predict 13B/32B models and shows promising transferability across pre-training datasets.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The investigated problem is of great practical value, reducing the computational cost for training large-scale LLMs.\n- The rBridge is generally effective and easy to implement, with details well covered.", "weaknesses": "- While the papers examine the choices of frontier models in the appendix (i.e., no meaningful difference), I am still concerned about whether the quality of the reasoning trajectories would influence the estimation. As frontier models remain a black box, it is challenging to understand the underlying training distribution. Would the prediction performance degrade significantly if the reasoning benchmarks are OD even for those frontier models?\n- The weighted NLL is not ablated. How much does it contribute to the final prediction performance?", "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": "Vz230rrMlN", "reviewer": "Reviewer_KWnN", "summary": "This paper offers an improved method for predicting large language model performance on reasoning tasks using results from smaller proxy models. RBridge is an elegant solution. First, reasoning traces from a frontier model are extracted for some benchmark dataset. Second, the negative log-likelihood of each token in the reasoning trace is computed using a small proxy language model. Finally, this token-wise NLL is weighted by MinMax normed average probability of each character in that token, as defined by the frontier model. This produces a weighted negative log-likelihood of the reasoning trace. The authors show that this value ranks and predicts performance of larger models. \n\nIn the first experiment, they train small proxy models and large target-size models on 25 different datasets. They compute the agreement (Decision Accuracy) between the rankings of which dataset leads to better performance (on specific benchmarks) for the proxy models and for the target models. They show that they outperform other methods for ranking datasets and incur a significant compute saving.\n\nIn the second experiment, they show that the RBridge value (weighted NLL) for proxy models at different stages of training is strongly correlated with and predictive of the performance of much larger models.\n\nIn the third experiment, they show that fitting a function to predict accuracy given the RBridge value (weighted NLL) based on one dataset, transfers seamlessly to predicting performance for a model trained on an entirely different dataset.\n\nOverall, this work offers an innovative and powerful new metric for optimising pre-training datasets through the use of small proxy models. This will be very useful to the community for improving LLM pre-training efficiently, because they can iterate on different training regimes using a small model, with the knowledge that they can predict the performance of much larger models.", "questions": "* Are letter-level probabilities within each token conditional on the previous characters/tokens?\n* The performance of $R^\\phi$ alone is surprisingly high (e.g., in Table 2). Why bother with the extra hassle of character-wise tokenization etc.? What argument can the authors make about the meaningfulness of the difference in performance between $R^\\phi$ and RBridge? To some, these differences might look negligible.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 4, "strengths": "The paper has many strengths:\n* The motivation and methodology are well thought through and sound.\n* The empirical work is diligent and complete. I commend the authors on the number of training and testing runs they must have performed to complete this project.\n* The results are very impressive. The efficiency gain from using RBridge compared to other methods is considerable and will greatly accelerate the development of optimal pre-training datasets for large-scale reasoning models.", "weaknesses": "There are only a few minor weaknesses with the paper at the moment:\n1. The authors miss some important literature on predictable AI and the use of assessor models for predicting model performance on unseen tasks. I include relevant references at the bottom.\n2. The writing is quite rough in several areas. I outline the key spelling/syntax problems below. I would recommend going over the whole paper to check for sense and grammaticality.\n3. I would include the limitations and future directions section in the main text, given the extra page.\n4. The authors are right to point out that obtaining the reasoning traces from frontier models is an extra cost and may be imperfect. I would definitely like to see the authors make their datasets public upon publication.\n5. This is not really a weakness, but the authors could emphasise more that their results really mean that LLM development can *greener*. Right now, iterating over different data regimes uses a lot of energy, which their method can help to reduce if the community makes use of RBridge.\n\n\n## Missing References\n\nKipnis, A., Voudouris, K., Buschoff, L. M. S., & Schulz, E. (2024). metabench--A Sparse Benchmark of Reasoning and Knowledge in Large Language Models. arXiv preprint arXiv:2407.12844.\n\nPacchiardi, L., Voudouris, K., Slater, B., Martínez-Plumed, F., Hernández-Orallo, J., Zhou, L., & Schellaert, W. (2025). PredictaBoard: Benchmarking LLM score predictability. arXiv preprint arXiv:2502.14445.\n\nPrudêncio, R. B., Lorena, A. C., Silva-Filho, T., Drapal, P., & Valeriano, M. G. (2024, June). Assessor models for explaining instance hardness in classification problems. In 2024 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.\n\nSchellaert, W., Martínez-Plumed, F., & Hernández-Orallo, J. (2025). Analysing the predictability of language model performance. ACM Transactions on Intelligent Systems and Technology, 16(2), 1-26.\n\n\n## Spelling/Syntax\n\nApproximation line numbers followed by the corrected sentence.\n\nLine 36: \"suggests that there may be a limit...\"\nLine 88: \"To bridge the evaluation scheme between small proxy models to large target-scale models...\"\nLine 94: \"We improve task alignment at ....\"\nLine 95 and the enumerated list: This needs to be fully revised for sense. It should start with \"We empirically validate our method in the following five ways:\" and then each number should be a full sentence.\nLine 269: \"which demonstrates that including an SFT stage...\"\nLine 286: \"can help target metric Acc./p@1 to be used as signals\"\nLine 286-7: The sentence on TED oes not make sense to me.\nLine 289: \"Last is ScB which visualizes...\"\nLine 299: \"respectively\"", "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)