Do reasoning models have "Aha!" moments? Prior work suggests that models like DeepSeek-R1-Zero undergo sudden mid-trace realizations that lead to accurate outputs, implying an intrinsic capacity for self-correction. Yet, it remains unclear whether such intrinsic shifts in reasoning strategy actually improve performance. Here, we study mid-reasoning shifts and instrument training runs to detect them. Our analysis spans 1M+ reasoning traces, hundreds of training checkpoints, three reasoning domains, and multiple decoding temperatures and model architectures. We find that reasoning shifts are rare, do not become more frequent with training, and seldom improve accuracy, indicating that they do not correspond to prior perceptions of model insight. However, their effect varies with model uncertainty. Building on this finding, we show that artificially triggering extrinsic shifts under high entropy reliably improves accuracy. Our results show that mid-reasoning shifts are symptoms of unstable inference behavior rather than an intrinsic mechanism for self-correction.
Anecdotal evidence suggests that language models fine-tuned with reinforcement learning exhibit "Aha!" moments-episodes of apparent insight reminiscent of human problem-solving. For example, Guo et al. (2025) highlight mid-trace cues such as "Wait... let's re-evaluate step-by-step," which sometimes accompany correct answers. Yet, the nature, frequency, and impact of these events (Fig. 1) remain unclear (Yang et al., 2025).
The existence of “Aha!” moments is linked to whether reasoning models can intrinsically selfcorrect, i.e., revise their reasoning mid-response without external feedback. Model improvements often arise from extrinsic mechanisms like verifiers,
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If Alice is older than Bob & Bob is older than Charlie, who is the oldest?
Step 1… Wait! actually… A: Alice Figure 1: Anatomy of an “Aha!” Moment. We illustrate an “Aha!” moment as described in Guo et al. (2025): within a single chain-of-thought, a cue such as “Wait… let’s re-evaluate” marks a shift from an initially failing strategy (k ∈ {1, 2}) to one that yields a correct answer (when k = 3). The figure also anticipates our methodology: we study “Aha!” moments by systematically GRPO-tuning and annotating the reasoning traces of Qwen2.5 and Llama models. reward queries, prompting techniques, or external tools (Lightman et al., 2024;Li et al., 2024a;Zhang et al., 2024). In contrast, intrinsic self-improvement must be inferred from reasoning traces and is arguably more safety-relevant, as it implies that a model can reorganize its reasoning from internal state alone (Tsui, 2025;Liu et al., 2025).
Studying the effect of reasoning shifts is challenging. First, these events may occur (and affect performance) during training, yet evaluations are typically conducted only post-training (Zeng et al., 2024;Xia et al., 2025). Second, reasoning models rarely release mid-training checkpoints, limiting longitudinal analyses across the training lifecycle. Third, even when shifts are observed, attributing correctness to a mid-trace change (rather than to general competence or memorization) requires systematically controlled comparisons. This gap motivates the need for a systematic investigation of whether reasoning shifts reflect genuine insight.
Present work. Here, we investigate whether mid-trace reasoning shifts (e.g., “Wait… let’s reevaluate”) signal intrinsic self-correction in reasoning models. Our study is guided by three questions: RQ1: Do reasoning shifts raise model accuracy? RQ2: How does the effect of reasoning shifts vary with training stage and decoding temperature? RQ3: Are reasoning shifts more effective when reasoning models are uncertain?
To answer these, we (i) formalize “Aha!” moments as measurable mid-trace shifts in reasoning that improve performance on problems that were previously unsolved by the model (Yang et al., 2025;Zhou et al., 2025;Hu et al., 2025) (Fig. 2; §3); (ii) curate a diverse evaluation suite ( §4) spanning cryptic crosswords (Efrat et al., 2021), mathematical problem-solving (MATH-500) (Lightman et al., 2024), and Rush Hour puzzles (Fogleman, 2018); and (iii) GRPO-tune and annotate the reasoning traces of Qwen2.5 and Llama models ( §5).
Our analysis spans 1M+ annotated reasoning traces across hundreds of checkpoint evaluations (10-20 per model/run), 3 domains, 4 temperatures, 2 model sizes, and 2 model architectures, providing a longitudinal view of how mid-trace reasoning evolves during RL fine-tuning. With this setup, we connect shift behavior to both correctness and token-level uncertainty signals (Ton et al., 2025).
Our results show that reasoning shifts are rare (overall ∼6.31% of traces) and generally do not improve model accuracy (RQ1). We further find that their impact on accuracy does not reliably flip sign across training stages, but varies substantially with decoding temperature (RQ2). Finally, we find that spontaneously occurring shifts do not become reliably helpful under high uncertainty; however, externally triggered reconsideration under high entropy improves accuracy across benchmarks, including a +8.41pp improvement on MATH-500 (and smaller gains on crosswords and Rush Hour) (RQ3). Our results are robust across datasets, prompts, and model families.
Contributions. We make three key contributions:
Definition & framework. We formalize “Aha!” moments as measurable mid-trace shifts and introduce an experimental framework for studying intrinsic self-correction during RL fine-tuning.
Empirical characterization at scale. Across 1M+ traces spanning domains, temperatures, training stages, and model families, we show that reasoning shifts are rare and typically coincide with lower accuracy, challenging the view that they reflect genuine insight.
Intervention. We develop an entropy-gated intervention that induces reconsideration when models are uncertain, yielding measurable accuracy gains.
Emergent Capabilities. Large language models often appear to a
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