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arxiv: 2508.13654 · v6 · submitted 2025-08-19 · 💻 cs.LG · cs.AI· cs.CL

Input-Time Scaling: Adding Noise and Irrelevance into Less-Is-More Drastically Improves Reasoning Performance and Efficiency

Rapheal Huang (Yuming) , Weilong Guo This is my paper

Pith reviewed 2026-05-18 22:38 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords LLM reasoningdata qualitynoise additiontraining-testing co-designInput-Time ScalingAIME benchmarkreasoning efficiency
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The pith

Adding noise and irrelevant contexts consistently across training and inference improves LLM reasoning performance and efficiency.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper demonstrates that LLMs reason more effectively when relevant and irrelevant contexts are mixed in the same way during both training and testing. This training-testing co-design enables the use of small low-quality datasets on capable models to achieve strong results on challenging math problems. It also enhances reasoning efficiency at no extra cost. The proposed Input-Time Scaling method keeps the advantages of small data while eliminating the need for careful quality curation.

Core claim

Mixing relevant and irrelevant contexts consistently across training and inference stages yields optimal results, with low-quality data benefiting capable models on hard questions, leading to the Input-Time Scaling approach that achieves 76.7% pass@1 on AIME24/25 using Qwen2.5-32B-Instruct.

What carries the argument

Training-testing co-design, which applies the same mix of relevant and irrelevant persona contexts in training data and inference queries to optimize reasoning.

If this is right

  • High-quality data benefits weaker models on easy questions, whereas low-quality data achieves higher scores on hard questions with capable models.
  • Reasoning performance is linked to reasoning efficiency when noisy contexts are added.
  • The method maintains Less-Is-More benefits while removing labor-intensive quality curation.
  • State-of-the-art performance is reached among Qwen2.5-32B variants on AIME benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The co-design approach may generalize to other reasoning domains such as programming or scientific inquiry.
  • It could enable more cost-effective fine-tuning by leveraging uncurated data sources.
  • Optimal levels of added noise might be discovered through systematic variation with model size.

Load-bearing premise

The gains are specifically due to the training-testing co-design with controlled noise rather than differences in model prompting, evaluation protocols, or dataset details.

What would settle it

Testing the same setup but without consistent noise and relevance matching between training and inference to see if performance drops below high-quality data results would falsify the central claim.

read the original abstract

Large Language Models (LLMs) excel at reasoning, traditionally requiring high-quality large-scale data and extensive training. Recent works reveal a very appealing Less-Is-More phenomenon where very small, carefully curated high-quality datasets match resource-intensive approaches. In this work, we further systematically relax their quality constraints by adding controlled noise via persona context relevance and comparing datasets of different qualities. Counterintuitively, we find that mixing relevant and irrelevant contexts consistently across training and inference stages yields optimal results -- a phenomenon we term training-testing co-design. Dataset quality comparisons show that high-quality data benefits weaker models on easy questions, while low-quality data achieves higher scores on hard questions with capable models. Across our experiments, reasoning performance is linked to reasoning efficiency. We, for the first time, found adding noisy and irrelevant contexts into queries can improve reasoning efficiency without any prices and targeted designs. Building on these insights, we propose Input-Time Scaling: applying small, low-quality data to capable models with training-testing co-design. This maintains Less-Is-More while further removing labor-intensive quality curation and improving reasoning effectiveness and efficiency, making the approach more applicable and affordable. Our method achieves 76.7% pass@1 on AIME24/25 using Qwen2.5-32B-Instruct, and 90.0%/80.0% with DeepSeek-R1-Distill-Qwen-32B -- state-of-the-art among Qwen2.5-32B variants. We are open-sourcing our datasets, pipelines, evaluation results, and checkpoints to facilitate reproducibility and further research.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes 'Input-Time Scaling' as an extension of the Less-Is-More phenomenon in LLM reasoning. By adding controlled noise via persona context relevance and enforcing consistent mixing of relevant and irrelevant contexts across training and inference (termed training-testing co-design), the authors show that small low-quality datasets can outperform high-quality ones on hard questions with capable models. They report achieving 76.7% pass@1 on AIME24/25 using Qwen2.5-32B-Instruct and 90.0%/80.0% with DeepSeek-R1-Distill-Qwen-32B, claiming state-of-the-art among Qwen2.5-32B variants, along with improved reasoning efficiency. The work includes comparisons of dataset qualities and is accompanied by open-sourced datasets, pipelines, and checkpoints.

Significance. If the results hold under rigorous controls, this would be a significant contribution by demonstrating that deliberate introduction of noise and irrelevance can enhance reasoning performance and efficiency in LLMs without additional computational costs or complex designs. It relaxes the quality curation requirements of prior Less-Is-More approaches, potentially making high-performance reasoning more practical and affordable. The explicit linkage between performance and efficiency, and the open-sourcing of resources for reproducibility, strengthen the work's impact in the field of efficient LLM training and inference.

major comments (2)
  1. Abstract and Experiments: The headline result of 76.7% pass@1 on AIME24/25 with Qwen2.5-32B-Instruct via small low-quality data and training-testing co-design lacks an explicit ablation or statement confirming that prompting, temperature, number of shots, and evaluation harness are identical across all compared methods and baselines. Without this control, the causal link to the proposed noise addition and co-design cannot be firmly established, as differences in these factors could confound the observed gains.
  2. Method: The paper describes adding noise 'via persona context relevance' but does not provide a specific equation, algorithm, or table that quantifies relevance scores or the noise/relevance ratio used in the optimal mix. This makes it challenging to assess whether the optimality is an observed outcome or potentially influenced by unstated choices in dataset construction.
minor comments (2)
  1. Abstract: The abstract mentions 'for the first time' finding that noisy contexts improve efficiency without prices; this claim would benefit from a brief reference to prior related work on noise in training to contextualize novelty.
  2. Overall: Some figures or tables comparing dataset qualities could be clarified with error bars or statistical significance tests to support the claims about high-quality vs low-quality data benefits.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript to incorporate clarifications that strengthen the presentation of our results and methods.

read point-by-point responses

  1. Referee: Abstract and Experiments: The headline result of 76.7% pass@1 on AIME24/25 with Qwen2.5-32B-Instruct via small low-quality data and training-testing co-design lacks an explicit ablation or statement confirming that prompting, temperature, number of shots, and evaluation harness are identical across all compared methods and baselines. Without this control, the causal link to the proposed noise addition and co-design cannot be firmly established, as differences in these factors could confound the observed gains.

    Authors: We agree that an explicit statement on evaluation consistency is necessary to firmly attribute gains to the proposed input-time scaling and training-testing co-design. All experiments in the paper, including baselines and our method, were conducted under identical conditions: the same prompting templates, temperature set to 0 for deterministic decoding, consistent few-shot or zero-shot settings matching the task requirements, and the same evaluation harness and scripts for AIME24/25. To make this transparent, we will add a new paragraph in the Experiments section (and reference it in the abstract) that explicitly lists these shared settings and confirms uniformity across all compared approaches. This revision will eliminate any potential ambiguity regarding confounding factors. revision: yes

  2. Referee: Method: The paper describes adding noise 'via persona context relevance' but does not provide a specific equation, algorithm, or table that quantifies relevance scores or the noise/relevance ratio used in the optimal mix. This makes it challenging to assess whether the optimality is an observed outcome or potentially influenced by unstated choices in dataset construction.

    Authors: We acknowledge that a more precise, quantitative description of the relevance mechanism and mixing ratios would improve clarity and reproducibility. The current manuscript describes the high-level process of introducing controlled noise through persona contexts of varying relevance, but we will expand the Method section to include: (1) pseudocode or an algorithm box outlining how relevance is assigned (via semantic similarity metrics combined with manual verification for persona quality), and (2) a table reporting the specific noise/relevance ratios tested and the optimal mix (e.g., the proportion of irrelevant contexts) that produced the reported results. These additions will make the construction process fully transparent and allow readers to verify that the optimality stems from systematic exploration rather than unstated choices. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical comparisons with no definitional reductions

full rationale

The paper reports empirical results from dataset quality comparisons and mixing of relevant/irrelevant persona contexts across training and inference stages. Performance numbers such as 76.7% pass@1 on AIME24/25 are presented as observed outcomes of these experiments rather than quantities derived from equations or fitted parameters that reduce to the inputs by construction. No mathematical derivation chain, self-definitional relations, or load-bearing self-citations appear in the abstract or described claims. The work remains self-contained through direct experimental reporting and open-sourced artifacts.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The work depends on the prior Less-Is-More observation and on the empirical finding that noise addition is beneficial; no new mathematical axioms or invented physical entities are introduced.

free parameters (1)
  • noise/relevance ratio
    The level of irrelevant persona context is described as controlled but must be chosen to achieve the reported optimal mix.
axioms (1)
  • domain assumption The Less-Is-More phenomenon holds as a starting point for the models and tasks studied.
    The paper explicitly builds on recent works that reveal this phenomenon.

pith-pipeline@v0.9.0 · 5830 in / 1412 out tokens · 44056 ms · 2026-05-18T22:38:33.984061+00:00 · methodology

discussion (0)

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Reference graph

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