{"id":"6b39a567-f51b-451c-b6cf-04cfde518fe1","arxiv_id":"2412.07111","paper_version":1,"verdict":"REJECT","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"The paper claims that proxy tasks selected by cross-model performance correlation and small-model variance ratios can predict LLM tool-use capability rankings at early training stages.","lead":"This paper proposes a two-step method to predict a large language model's future performance on complex 'emergent' tasks, such as tool use, by evaluating it on simpler proxy tasks early in training. The authors test it on five models and report that proxy-task rankings align with the actual tool-use rankings, but the prediction formula and thresholds are not disclosed.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Proxy-task relevance is estimated on 17 public model pairs but validated on only 5 same-architecture Qwen runs; without cross-model transfer evidence the central claim of predictable emergent abilities is unsupported.","rationale":"I agree with the reader that the weakest assumption is the transfer of proxy-task relevance from existing public models to newly trained models. The paper's validation is limited to five Qwen-based runs from one architecture with only ranking consistency reported, which cannot establish that the selected proxies are predictive for other architectures, data mixtures, or training schedules. The leave-one-out experiment over the existing 17 model pairs is the most direct way to test this, and it uses data the authors already have. The missing prediction formula and unspecified thresholds are additional reproducibility problems, but the transfer question is the more fundamental scientific gap. If the leave-one-out test passes, the paper's central claim would be considerably strengthened; as it stands, the evidence does not support the general claim of predictable emergent abilities, so the reader's REJECT verdict remains appropriate.","tokens_in":12378,"tokens_out":8278,"duration_ms":89000,"concrete_test":"Run leave-one-out cross-validation over the 17 model pairs in Table 2: for each held-out pair, recompute proxy-task relevance using the remaining 16 pairs, apply the Section 4.2 robustness filter, and compute the Vbc-style predicted score for the held-out model. Compare predicted T-eval rankings against actual T-eval rankings across all 17 folds; report mean Kendall tau and its bootstrap confidence interval. If the mean is not significantly positive, the selected proxies do not transfer across models. A complementary check would repeat the case study on at least one non-Qwen family (e.g., Llama-2 or Mistral) to test architecture independence.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing assumption is that relevance and robustness measured on existing public models transfer to newly trained models. Section 4.3 tests this with only five 18B-parameter Qwen checkpoints, all sharing one architecture and one SFT recipe, and reports only rank-inversion counts. That is insufficient: with five items, a one-pair inversion is about p=0.04 before multiple-comparison correction, and no error bars or magnitude predictions are provided. The predicted score itself is also under-specified: thresholds for relevance and robustness (epsilon_C, epsilon_R) and the sigmoid scale k in Section 3.3 are never given, and the 'Proxy Task / Vbc' scores in Tables 6, 8, and 10 have no closed-form definition, so the claimed integration cannot be independently reproduced. If the proxy-task correlation structure shifts across architectures, data mixtures, or SFT protocols, the selected six tasks may track only Qwen-specific training effects rather than general tool-use ability. The paper's central claim therefore rests on an untested transfer assumption that the current 5-model case study cannot validate.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a method for predicting emergent LLM abilities from proxy tasks before the ability has developed. It defines task relevance as the correlation of normalized performance vectors across 17 public chat/base model pairs, validates candidate tasks using a robustness score computed from small Qwen-1.8B models trained on different data mixtures or random seeds, and then claims to integrate relevance and robustness into a weighted proxy score. In a case study on T-eval tool utilization, the authors compare proxy-task rankings of five Qwen-18B pretraining checkpoints (two annealing variants and three data-mixture variants) with post-SFT T-eval rankings and report one reversed pair for their preferred Vbc strategy.","tokens_in":12627,"tokens_out":7167,"duration_ms":73403,"significance":"If the method were fully specified and validated, it would offer a practical way to monitor early-stage training checkpoints and to compare training configurations without running costly target-task evaluations. The idea of transferring correlation structures from public leaderboard models to new training runs is attractive, and the two-stage relevance-plus-robustness selection is clearly presented. The manuscript's strengths are its concrete case study, its use of publicly available benchmark results, and the reproducible small-model robustness experiments. However, the current evidence does not establish the central claim: the final prediction score is not given in closed form, the relevance and robustness thresholds are free parameters, the validation set consists of five same-architecture models, and the robustness metric's interpretation is questionable. As presented, the work is a proposal with an illustrative case study rather than a validated predictive method.","major_comments":[{"comment":"The final prediction score is not defined in closed form. Section 3.3 defines Si = Ci * f(Ri) and normalized weights Wi, but the manuscript never reports the values of epsilon_C, epsilon_R, k, or the normalization used to produce the 'Proxy Task' and 'Vbc' numbers in Tables 6, 8, and 10. Since the Vbc column of Table 10 reproduces exactly the 'Proxy Task' rows of Tables 6 and 8, a reader cannot determine whether the published predictions actually implement the proposed relevance-robustness weighting, and the claimed integration cannot be independently reproduced. Please provide the exact formula and all parameter values, together with a sensitivity analysis of the results to those parameters.","section":"Section 3.3, Tables 6, 8, 10"},{"comment":"The validation is too narrow to support the claim of a 'strong correlation'. The five models are all 18B-parameter Qwen models with the same architecture and the same SFT recipe, and the only reported agreement statistic is the number of reversed pairs (1/10 for Vbc). With five items, a single inversion yields Kendall's tau = 0.8, and since three prediction strategies are compared, the evidence is marginal even before considering that the relevance weights were estimated from the same model family in Section 4.1. No confidence intervals, no per-model absolute errors, and no held-out architecture or training configuration are reported. The transfer of the correlation structure from 17 public model pairs to new training runs is therefore asserted rather than demonstrated.","section":"Section 4.3, Table 10"},{"comment":"The robustness score Ri = sigma^2_d / sigma^2_r is not a measure of robustness in the usual sense. A task whose scores vary strongly across data-mixture pretraining runs is highly sensitive to data distribution, which is a form of instability; interpreting a high variance ratio as 'stronger robustness' is a non sequitur. The small ensemble sizes (three random-noise models and five data-variability models) also make the variance ratio noisy, and no uncertainty is reported. The selection of C3 and CHID as the most robust tasks may therefore reflect task-specific sensitivity to data mixture rather than reliability.","section":"Section 3.2"},{"comment":"The performance-vector notation is inconsistent and appears to be transposed. Section 3.1 defines m as the number of models and n as the number of tasks, but Section 4.1 says 'construct an m-dimensional performance vector, where m = 42', while 42 is the number of candidate tasks and the number of model pairs is 17. Please fix the notation and confirm that the correlation analysis uses the correct dimensions. Additionally, two rows in Table 2 pair models that do not match (Baichuan2-13B-Chat with Baichuan2-7B-Base, and Mistral-7B-Instruct-v0.2 with Mixtral-7B-v0.1); these mismatches should be corrected or justified, because they directly affect the estimated relevance values.","section":"Sections 3.1 and 4.1, Table 2"}],"minor_comments":[{"comment":"Figure 1 is referenced in the text but its content is not described; if it contains the top-task rankings, the reader cannot verify the claim that reasoning and problem-solving tasks dominate the top ten without the figure.","section":"Section 4.1, Figure 1"},{"comment":"The citation for OpenCompass appears to be incorrect: the benchmark suite OpenCompass is not authored by Buitrago and Nystrom (2019), and the reference should be replaced with the appropriate OpenCompass documentation or paper.","section":"Section 2"},{"comment":"The abstract and conclusion claim a 'strong correlation' between predicted and actual performance, but no correlation coefficient, confidence interval, or significance test is reported; the rank-inversion count alone is insufficient to support that wording.","section":"Section 4.3"},{"comment":"The thresholds epsilon_C and epsilon_R are introduced but never specified or varied; if they are chosen by hand on the validation set, the method is not parameter-free, and the reported results may be optimistic.","section":"Section 3.3"}],"recommendation":"major_revision","confidential_remarks":"The central idea has merit, but the manuscript is not ready in its current form. The most serious problems are the missing closed-form prediction formula, the arbitrary thresholds, the narrow same-architecture validation, and the questionable robustness definition. These are all addressable in a revision: the authors should state the exact formula, report all parameter values and sensitivity analyses, and validate on held-out architectures or training configurations. If those additions are provided, a positive assessment may be possible; with the present evidence, the claim of predictable emergent abilities is not established."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this paper has a genuinely new two-stage method for picking proxy tasks, but the empirical evidence for the central claim is a lot weaker than the abstract suggests. If you're deciding whether to read it, treat it as an interesting case study, not a confirmed technique.\n\nWhat's new: the combination of cross-model relevance (Kendall correlation on performance vectors) with a variance-ratio robustness score from small model ensembles is not in the cited literature. The authors also did real work: they assembled 17 chat/base pairs for relevance, trained eight 1.8B models from scratch for robustness, and then trained five 18B Qwen variants to test prediction. That's more effort than most 'emergent ability' papers put in, and the writing is generally clear about the two stages.\n\nSoft spots: the prediction formula is under-specified. The relevance threshold, robustness threshold, and sigmoid slope k are never given, and the aggregated 'Proxy Task' score in Tables 6, 8, and 10 has no closed-form definition, so no one can reproduce the integration. The validation is five Qwen-18B runs, all from one architecture and one SFT recipe. That makes the transfer assumption real: relevance computed on public model pairs may not hold for a different architecture or training setup. With five items, one reversed pair out of ten is not strong evidence of a 'strong correlation,' and there are no error bars or magnitude predictions. The relevance weights are also fit to T-eval on the public model pairs, so the prediction is partly fitted to the target; the held-out models give some independence, but not enough to establish generalizability. There are also minor notation issues (m=42 vs 17 pairs; a mismatched Baichuan pair in Table 2), but those are fixable.\n\nThe reader's take is basically right that the central claim is not well-supported as stated. I'd add that the method is plausible and the experiments are real, so I'd rather see this go to a serious referee than get desk-rejected. The paper is for people who build early-stage evaluation pipelines or do model selection for tool-use; they may get useful ideas from the method, but they shouldn't rely on the current evidence as validation. If I were handling it, I'd send it to review with the expectation that the authors need to specify the full prediction rule and add cross-architecture validation before it's convincing.","headline":"A plausible two-stage proxy-task selection method, but the validation is too thin to back the strong-correlation claim; still worth a referee.","tokens_in":13118,"tokens_out":3087,"would_cite":false,"duration_ms":31657,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Emergent abilities like tool use can be predicted early in training from carefully chosen simple proxy tasks, and the paper shows the prediction holding across five differently trained models.","keywords":["emergent abilities","proxy tasks","tool utilization","early-stage evaluation","performance prediction","scaling laws","task relevance","robustness analysis"],"falsifier":"Train a new family of models with a different architecture or substantially different data mix, and compare the paper's early proxy ranking against their final T-eval ranking; if the proxy ordering no longer matches, the relevance structure has shifted and the method fails. A cheaper check is to recompute the Kendall relevance rankings of the 42 candidate tasks on a fresh set of public models and see whether the top proxy tasks remain the same.","tokens_in":12209,"feed_emoji":"🛠️","tokens_out":8619,"duration_ms":75245,"temperature":0.7,"pith_summary":"The paper takes aim at a known blind spot of scaling laws: they predict steady gains on tasks small models already perform, but not 'emergent' abilities that are absent in small or early-stage models. Its proposal is to predict such abilities anyway, by selecting simple, non-emergent tasks that are strongly and stably correlated with the target ability and blending their early-training scores into a single metric. Selection is data-driven: candidate tasks are ranked by how their scores across many existing models track the target benchmark's scores, then filtered by how robust each task is across small ensembles of models trained with different data versus different seeds. In a case study on tool use, the resulting early metric ranks five large models, trained with different annealing schedules and data mixes, almost exactly in the order of their final tool-use evaluations, while perplexity ranks them far less reliably. The paper's claim is that emergent abilities are predictable in advance if one measures the right simple tasks early.","feed_headline":"Proxy tasks predict tool-use skills before they emerge","feed_subtitle":"Six simple benchmarks rank LLM training runs in line with final tool-use scores, where perplexity fails.","key_machinery":"The load-bearing object is a two-stage selection pipeline built on a double-normalized performance matrix. Each candidate task is represented by a vector of scores across many models, normalized first across tasks and then across models, so that task difficulty and model scale do not dominate the comparison. Relevance to the target task is the Kendall rank correlation between the task vector and the target vector, chosen over Pearson and Spearman through a resampling check that keeps the variants whose top-ranked tasks survive random re-sampling of models. Robustness is the variance ratio $R_i = \\sigma_{di}^2 / \\sigma_{ri}^2$ between a task's scores on small models trained with varied data and on small models trained from varied seeds with identical data, the idea being that a stable proxy should be driven by data distribution, not random noise. The final proxy weight combines relevance with a sigmoid-transformed robustness score, and the early prediction is the normalized weighted average of the surviving tasks' scores.","core_discovery":"The central claim is that the emergent ability of tool utilization, which scaling laws cannot forecast, is predictable from proxy tasks evaluated early in training. The authors build an early-stage metric from six selected multiple-choice tasks, weighting each by its Kendall correlation with the T-eval benchmark across 17 model pairs and by a robustness ratio that compares a task's variance across data-varied small models with its variance across seed-varied small models. They then show that this metric orders five large models, trained with or without learning-rate annealing and under three data-selection strategies, in nearly the same sequence as their final T-eval scores: only one pair of ten is reversed, versus four reversals for a perplexity-based ranking. This is presented as evidence that complex capabilities can be evaluated, and training configurations optimized, during the early phases of model development rather than after full training.","pith_inferences":["If the relevance structure proves stable across training regimes, the proxy metric could serve as an optimization signal for data-mix and schedule choices, not just a diagnostic.","The variance-ratio robustness test is a general screening tool that could identify which benchmarks deserve trust at small scale, apart from any prediction goal.","The results support a picture in which tool use is not a discontinuous capacity but a composite of measurable precursors, suggesting that apparent 'emergence' may partly be an artifact of evaluation scale.","A stress test the paper does not run is applying the full pipeline to a second emergent ability and to a non-Qwen model family before the method is relied on."],"forward_implications":["Training configurations can be compared and adjusted at early checkpoints using the proxy metric, instead of waiting for full pretraining and fine-tuning.","Perplexity, the standard early-training metric, is a weaker ordering signal for tool-use capability than the selected proxy blend.","The same selection pipeline applies to other emergent abilities by substituting the target benchmark and the candidate task pool.","Because candidate-task scores are drawn from public leaderboards, choosing proxies requires no additional large-model evaluations."],"supporting_citations":[{"why":"Provides T-eval, the target benchmark for tool-utilization capability that the proxy metric must predict.","marker":"(Chen et al., 2023)"},{"why":"Supplies the OpenCompass leaderboard, the source of standardized public performance scores on the 42 candidate tasks.","marker":"(Buitrago and Nystrom, 2019)"},{"why":"Defines the Qwen-1.8B architecture used to build the small-model ensembles for robustness validation.","marker":"(Bai et al., 2023)"},{"why":"Introduces the emergent-abilities framing that defines the prediction problem the method addresses.","marker":"(Wei et al., 2022a)"},{"why":"Establishes scaling laws whose failure to predict emergent abilities is the gap the method fills.","marker":"(Henighan et al., 2020)"},{"why":"Supplies the theoretical view of complex task performance as a combination of basic competencies, motivating proxy-task prediction.","marker":"(Arora and Goyal, 2023)"},{"why":"Provides Falcon RefineWeb, one of the data sources for the Data Variability and Random Noise small-model groups.","marker":"(Penedo et al., 2023)"},{"why":"Provides RedPajamaV2, used in the PPL-versus-Filtered-versus-Diversity training comparison and as a robustness training corpus.","marker":"(Okazaki et al., 2024)"}],"fun_headline_variants":["Proxy tasks foresee tool-use emergence early in training","Early proxy benchmarks forecast LLM tool-use skills","Proxy tasks predict tool-use emergence before it happens","Scaling laws miss emergent skills, proxy tasks hit them"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The argument assumes that the task-relevance structure measured on 17 existing public model pairs carries over to newly trained models with different architectures, data mixtures, and training schedules, and the direct validation covers only five Qwen-based models under two kinds of training variation.","fun_headline_variants_meta":{"raw":{"variants":["Proxy tasks foresee tool-use emergence early in training","Early proxy benchmarks forecast LLM tool-use skills","Proxy tasks predict tool-use emergence before it happens","Scaling laws miss emergent skills, proxy tasks hit them"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000582,"raw_usage":{"total_tokens":2680,"prompt_tokens":830,"completion_tokens":1850,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":446,"completion_tokens_details":{"reasoning_tokens":1801}},"tokens_in":446,"tokens_out":1850,"duration_ms":16339,"temperature":1.0,"reasoning_tokens":1801,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T19:06:11.559751+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Train a new family of models with a different architecture or substantially different data mix, and compare the paper's early proxy ranking against their final T-eval ranking; if the proxy ordering no longer matches, the relevance structure has shifted and the method fails. A cheaper check is to recompute the Kendall relevance rankings of the 42 candidate tasks on a fresh set of public models and see whether the top proxy tasks remain the same.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the OpenCompass leaderboard, the source of standardized public performance scores on the 42 candidate tasks."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides Falcon RefineWeb, one of the data sources for the Data Variability and Random Noise small-model groups."}],"review_version":1}