---
title: Spontaneous Scale-Dependent Verbosity
url: https://www.emergentmind.com/topics/spontaneous-scale-dependent-verbosity
type: topic
---

# Spontaneous Scale-Dependent Verbosity

Searching arXiv for the cited papers on spontaneous scale-dependent verbosity and verbose CoT.
Spontaneous scale-dependent verbosity denotes a scale-linked generation pattern in language models in which larger models, under neutral prompting, tend to produce longer and more elaborate responses than smaller models, and this additional elaboration can either degrade performance through overreasoning or yield only modest gains unless the extra tokens carry useful reasoning and validation content [2604.00025]. In the literature, the term is used in two closely related but distinct senses. One sense identifies a harmful failure mode under standard benchmark prompting, where verbosity emerges without explicit chain-of-thought instruction and can reverse apparent capability rankings across model sizes [2604.00025]. The other sense treats verbosity as an experimental variable in chain-of-thought studies and asks whether longer traces help merely by being longer; the available evidence argues against a pure length-based account and attributes any benefit primarily to semantic reasoning content, especially checking and validation steps [2606.30128].

## 1. Definition and conceptual scope

In the benchmark-evaluation setting, spontaneous scale-dependent verbosity refers to a pattern where larger models produce outputs that are too verbose for the task, often with implicit reasoning that becomes error-prone [2604.00025]. The phenomenon is termed “spontaneous” because it appears under intentionally bare prompts rather than under explicit chain-of-thought elicitation. The canonical example is a control prompt of the form `Problem: {problem_text}\n\nSolution:` for GSM8K, or simple question/answer templates for multiple-choice tasks; under these conditions, any extra elaboration is treated as an intrinsic, scale-dependent generation tendency rather than a prompt artifact [2604.00025].

A distinct but related usage appears in work on verbose chain-of-thought. There, the question is whether longer reasoning traces help because they contain useful intermediate reasoning or because extra tokens provide more serial computation before answer commitment [2606.30128]. The relevant contrast is between a **semantic-trace hypothesis**, according to which chain-of-thought helps because the trace explicitly contains useful intermediate reasoning, and a **forward-pass-computation hypothesis**, according to which extra tokens help because they buy additional serial computation [2606.30128]. The evidence favors a content-driven view with a possible secondary role for prose quality, rather than a pure length/computation account [2606.30128].

Taken together, these uses identify a common theme: response length is not an autonomous proxy for capability. A plausible implication is that verbosity is best understood as a task- and scale-sensitive behavioral tendency whose effect depends on the semantic function of the added text rather than on token count alone.

## 2. Emergence under standard prompting

The strongest formulation of the harmful-verbosity account comes from the study of inverse scaling under universal prompts. That work evaluates **31 models** spanning **0.5B to 405B parameters** on **1,485 problems** from **GSM8K**, **BoolQ**, **ARC-Easy**, **CommonsenseQA**, and **MMLU-STEM**, using **greedy decoding** only with `do_sample=False`, top-p disabled, top-k disabled, repetition penalty = 1.0, and max output tokens = 512 [2604.00025]. Models are grouped as **small** when \(N \le 10\)B and **large** when \(N \ge 70\)B [2604.00025].

Within this setup, the paper identifies **115 inverse-scaling problems**, or **7.7%** of all evaluated items, on which smaller models outperform larger ones [2604.00025]. On those problems, the reported average advantage is **28.4 percentage points** in favor of small models, with **Cohen’s \(d = 1.34\)**, and the aggregate comparison gives **66.1%** accuracy for small models versus **41.5%** for large models, a **24.6-point** degradation [2604.00025]. The paper also reports a continuous scale–performance relation on inverse problems with **Pearson \(r = -0.388\), \(p = 0.0035\)**, indicating that performance tends to worsen as parameter count increases across the full spectrum [2604.00025].

The proposed mechanism is overelaboration. Large models on inverse-scaling problems produce **202 tokens** on average, compared with **127 tokens** for small models, while producing **slightly fewer explicit reasoning steps**: **9.1 steps** for large models versus **10.5 steps** for small models [2604.00025]. This distinction is central. The phenomenon is not described as “more reasoning steps” in a narrow explicit sense; instead, it is characterized as **more expansive implicit reasoning**, explanation, qualification, and elaboration [2604.00025]. The paper further reports that response length is negatively correlated with large-model accuracy on inverse-scaling problems, with **\(r = -0.43\)** [2604.00025].

This suggests that the critical variable is not simply whether a model reasons, but whether its default style becomes overprocessed relative to task requirements. The paper explicitly frames the issue as a prompt-sensitivity problem rather than an intrinsic inability, arguing that larger models’ competence is being **masked** by universal prompting that does not match the model’s scale [2604.00025].

## 3. Operationalization and empirical signatures

The literature operationalizes the phenomenon through response length, accuracy, problem-level inverse-scaling gaps, and controlled comparisons of concise versus verbose traces. In the benchmark study, response length is measured as the token count of the generated output, excluding prompt tokens, summarized by model category through the average response length \(\bar{L}\), and length diversity in contamination analysis is quantified with the **coefficient of variation** \(\mathrm{CV}_i = \sigma_{L_i}/\mu_{L_i}\) [2604.00025]. Accuracy is defined as
\[
\text{Acc}_m = \frac{1}{N} \sum_{i=1}^{N} \mathbb{1}[\hat{y}_{m,i} = y_i],
\]
and the inverse-scaling gap for a problem is
\[
\Delta_i = \text{Acc}_{\text{small},i} - \text{Acc}_{\text{large},i},
\]
so that positive \(\Delta_i\) indicates superior small-model performance [2604.00025].

In the chain-of-thought study, the principal effect size is instead
\[
\Delta = \mathrm{acc(verbose)} - \mathrm{acc(concise)}.
\]
This quantity is used in two experimental legs: an in-distribution same-model comparison between shorter and longer natural generations on the same question, and a controlled intervention in which concise and verbose traces are rewritten while holding the underlying computation fixed [2606.30128].

For the in-distribution leg, the crucial criterion is that paired traces must follow the **same reasoning plan**. On GSM8K, the paper applies a pre-filter based on a **computation-graph signature**, specifically a **Weisfeiler–Lehman hash** over the arithmetic operation graph, matching traces that perform the same operations in the same dependency structure regardless of numeric literals [2606.30128]. A local LLM judge, **E3**, then confirms same-plan equivalence on number-redacted traces; on MATH-500, E3 alone is used [2606.30128]. Pairing additionally requires a generated-token ratio \(\ge 1.5\) [2606.30128].

For the controlled intervention, semantic equivalence is defined through **directed acyclic graph (DAG) equivalence**. Two arithmetic traces are DAG-equivalent if they contain the same computational nodes, in the same dependency order, with identical intermediate values, regardless of whether they are expressed concisely or verbosely [2606.30128]. Validation is carried out with two validators: **E2**, an algorithmic, DAG-informed validator for arithmetic benchmarks only, calibrated via logistic regression on six features, and **E3**, a local LLM judge that returns one of `{equivalent, not_equivalent, ambiguous}` with confidence on number-redacted traces [2606.30128]. Statistical procedures include **question-clustered bootstrap** in Leg 1, **paired bootstrap** in Leg 2, **1,000-sample** or **10,000-sample** bootstrap confidence intervals depending on the leg, **stratification by source × anchor correctness × validator**, and cross-checks with a **frontier judge (Claude Opus 4.8)** and a non-Qwen judge (**Yi-1.5-9B**) for robustness [2606.30128].

## 4. Causal interventions: brevity constraints and controlled verbosity

The causal evidence for harmful spontaneous scale-dependent verbosity comes from explicit brevity constraints imposed on inverse-scaling problems [2604.00025]. The intervention is run on all **115 inverse-scaling problems** using **seven models**: **3 small** models—Qwen2.5-0.5B, Llama-3.2-3B, Gemma-2-2B—and **4 large** models—Llama-3.1-70B, Llama-3.1-405B, Qwen2.5-32B, DeepSeek-67B [2604.00025]. Three conditions are compared: **Control**, **Brief**, and **Direct** [2604.00025]. The **Brief** condition specifies task-specific limits: for GSM8K, **under 50 words**; for BoolQ, **10 words or less**; and for multiple-choice tasks, **just the letter plus one sentence** [2604.00025]. The paper emphasizes that outputs are **not hard-truncated**; the brevity reduction is an empirical consequence of instruction-following [2604.00025].

Under control prompts, large models underperform small models by **44.2 percentage points**, with **40.2%** accuracy for large models and **84.4%** for small models [2604.00025]. Under **brief** prompts, the gap shrinks by **67%** to **14.8 points**: large models improve by **+26.3 points**, while small models decline only **−3.1 points** [2604.00025]. Under **direct** prompts, the gap narrows further to **7.8 points**, although both groups lose some accuracy relative to the brief condition, implying that some reasoning remains useful even when excessive reasoning is harmful [2604.00025]. The paper reports **paired \(t = 7.80\)** with **\(p < 0.0001\)** [2604.00025].

These behavioral changes are accompanied by large length reductions. For large models, median output length falls from **197 tokens** to **78 tokens** under brief prompts, a **60.4% reduction**, and to **57 tokens** under direct prompting, a **71.1% reduction** [2604.00025]. Small models reduce length by only about **15%**, whereas large models reduce it by about **60%**, which the paper interprets as evidence that verbosity is scale-dependent [2604.00025].

The controlled-intervention evidence on verbose chain-of-thought yields a different but complementary result. When semantic content is held fixed via DAG equivalence, verbose traces **do** improve accuracy, but the effects are **modest**: **25 of 32 benchmark-target cells** are positive under at least one validator, with typical effect sizes of **1–4 percentage points** under E3, while E2 estimates are generally **3–4× larger** than E3 [2606.30128]. The interpretation is therefore not that verbosity is useless, but that whatever benefit exists depends on the quality and structure of the prose rather than on token count alone [2606.30128].

## 5. Dataset patterns, reversals, and boundary conditions

The benchmark evidence shows that spontaneous scale-dependent verbosity is not uniformly distributed across tasks. Among the **115 inverse-scaling problems**, dataset-specific prevalence is reported as **BoolQ: 34/300 = 11.3%**, **CommonsenseQA: 29/300 = 9.7%**, **ARC-Easy: 28/300 = 9.3%**, **GSM8K: 13/300 = 4.3%**, and **MMLU-STEM: 11/285 = 3.9%** [2604.00025]. The paper also reports dataset-specific “optimal scales” ranging from **0.5B** for BoolQ and MMLU-STEM to **3.0B** for GSM8K [2604.00025]. This indicates that the best-performing model scale is not constant across benchmarks.

The most prominent finding is that brevity constraints can reverse performance hierarchies. On **GSM8K**, the control gap is **+13.1 pp** favoring small models, but the brief gap becomes **−7.7 pp** favoring large models [2604.00025]. On **MMLU-STEM**, the control gap is **+27.3 pp**, and the brief gap becomes **−15.9 pp** [2604.00025]. Strong but incomplete reductions are also reported for **ARC-Easy**, where the gap is reduced by **73.8%** from **71.4 pp** to **18.8 pp**, and for **CommonsenseQA**, where the reduction is **61.5%**, from **56.0 pp** to **21.6 pp** [2604.00025]. **BoolQ** is the explicit exception: its gap slightly increases from **23.5 pp** to **24.3 pp**, and the paper interprets this as evidence that brevity is not universally beneficial because some elaboration can be functional on passage-based tasks [2604.00025].

The chain-of-thought study also emphasizes boundary conditions. In the in-distribution same-plan leg, for **independently trained reasoners**, once the reasoning plan is fixed, extra tokens do essentially nothing: pooled **MATH-500** gives \(\Delta = -0.005\) with confidence interval \([-0.010, -0.000]\), which the paper describes as essentially zero [2606.30128]. Two exceptions are reported: **DeepSeek-R1-Distill released weights**, which show a small positive effect on **MATH-500** of **+0.019** with confidence interval \([+0.010, +0.027]\) and **+0.046** on **GSM8K** in one arm; and **non-reasoners** on GSM8K, such as **mistral-7B** at **+0.079** and **qwen3-0.6B-nothink** at **+0.046** [2606.30128]. The authors regard these cases as idiosyncratic rather than general across independently trained reasoners [2606.30128].

A further boundary condition comes from **maximum numerical redaction**, where all numbers are replaced by `<NUM>`. Under this intervention, the verbose-helps effect is amplified by a median factor of **\(3.24\times\)** across four arithmetic benchmarks, **30 of 32 cells** preserve sign, and **28 of 32** remain confidence-interval significant [2606.30128]. The paper provides examples including **SVAMP × Qwen3-4B self**, moving from near-zero to **+0.226**, and **OLMo on MultiArith shared**, increasing from **+0.169 → +0.547** [2606.30128]. The same study also reports that on **StrategyQA**, verbose chain-of-thought can hurt for some models, underscoring that the effect is not universal [2606.30128].

## 6. Mechanisms and interpretive disputes

Two competing explanations structure the current interpretation of the phenomenon. The first is a length-based or forward-pass-computation account, according to which longer generations help because they postpone answer commitment and provide more serial computation [2606.30128]. The second is a content-based account, according to which extra tokens help only insofar as they encode useful reasoning operations, intermediate values, or verification procedures [2606.30128].

The evidence weighs against a pure length-based interpretation. In the same-plan in-distribution analysis, fixing the reasoning plan largely eliminates any benefit of length for independently trained reasoners [2606.30128]. The study further reports that a blind analysis of surplus tokens shows that **pure verbosity**—described as “elaborate/restate”—never robustly distinguishes winners from losers, whereas **validation/checking content**, specifically “verify/check” and “plug-in/test,” does [2606.30128]. In the oracle-trace experiment, padding L1 with fluent **non-reasoning filler** leaves accuracy near L1 and does **not** approach L4 performance, while a length-matched semantic condition remains significantly better [2606.30128]. The paper therefore concludes that **token count alone is insufficient** [2606.30128].

The benchmark study makes a parallel argument in a different register. There, larger models are not described as lacking capability; rather, their capability is obscured by a generation style that is too elaborate under universal prompts [2604.00025]. The authors speculate that **RLHF-style training** may encourage length and “helpfulness,” and that larger models may be more likely to internalize and enact those signals, but they do not claim to have fully identified the root cause [2604.00025]. Their key conceptual point is that larger models may produce responses that appear more careful or complete yet are overprocessed for some benchmark items [2604.00025].

The two papers are therefore compatible. One shows that unprompted verbosity in larger models can be causally harmful under standard evaluation [2604.00025]. The other shows that, when verbosity is isolated from semantic content, its benefit is usually negligible or modest, and disappears when extra text is merely filler [2606.30128]. This suggests that the decisive factor is not “more tokens” in the abstract but whether the additional text performs task-relevant reasoning or checking.

## 7. Implications for evaluation and deployment

The evaluation consequences are explicit. Standard benchmark protocols can be systematically misleading for a subset of problems because **universal prompts** may favor smaller models on some items and mask larger models’ capabilities [2604.00025]. The benchmark study identifies four implications: benchmark scores under universal prompts can underestimate frontier models, especially on the **7.7%** of inverse-scaling problems; **problem-level analysis** matters because aggregate accuracy hides failures and reversals; **brevity-constrained evaluation** can reveal latent capability, especially on math and science reasoning benchmarks; and removing non-discriminative items could reduce evaluation cost by about **28%** while preserving discriminative power [2604.00025].

For deployment, the practical recommendation is **scale-aware prompt engineering** and **problem-aware routing** rather than a single prompt format for all models [2604.00025]. The proposed operational policy is to use smaller models where they suffice and are cheaper, apply brevity constraints selectively for large models on tasks prone to overelaboration, and route inputs based on problem type and prompt sensitivity [2604.00025]. Because the interventions improve both **accuracy** and **efficiency / computational cost**, the issue is treated not only as an evaluation artifact but also as a deployment concern [2604.00025].

The chain-of-thought results refine this recommendation by showing that shortening outputs indiscriminately is not equivalent to improving reasoning. Some reasoning is useful, but the benefit depends on whether the added text contributes **facts, operations, intermediate values, and validation/checking steps** [2606.30128]. A plausible implication is that optimal prompting should not simply minimize length; it should suppress overelaboration while preserving semantically functional reasoning structure.

In this sense, spontaneous scale-dependent verbosity names a specific failure mode of scale-sensitive generation behavior, while the broader empirical literature distinguishes harmful overelaboration from useful explicit reasoning. Larger models may appear worse under neutral prompts because they generate responses that are too expansive for certain tasks [2604.00025], yet longer traces do not help reliably unless they encode substantive reasoning or checking content [2606.30128]. The resulting picture is not that brevity is always superior, nor that verbosity is intrinsically helpful, but that the performance effect of length is mediated by task fit, model scale, and the semantic work performed by the added tokens.

Source: https://www.emergentmind.com/topics/spontaneous-scale-dependent-verbosity