Recognition: 2 theorem links
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
Pith reviewed 2026-05-17 11:35 UTC · model grok-4.3
The pith
Language models self-improve under RL when they already use reasoning behaviors like verification and backtracking, even if answers start wrong.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper claims that the presence of specific reasoning behaviors determines whether a language model can effectively use additional computation to self-improve via reinforcement learning. Qwen exhibits verification, backtracking, subgoal setting, and backward chaining from the start and improves steadily, while Llama lacks them and plateaus. Priming Llama with datasets that include these behaviors produces large gains during RL; the same gains appear even when the primed solutions are factually incorrect, showing that the reasoning patterns drive progress more than answer accuracy. Continued pretraining on filtered math data that amplifies the same behaviors brings Llama to the same self-
What carries the argument
The four cognitive behaviors—verification, backtracking, subgoal setting, and backward chaining—used as the central mechanism in controlled priming experiments and filtered pretraining to transfer self-improvement capacity.
If this is right
- Priming Llama with behavior-rich examples produces RL gains that match or exceed Qwen's performance.
- Incorrect solutions containing proper reasoning patterns yield comparable self-improvement to correct solutions.
- Continued pretraining on reasoning-behavior-rich data lets Llama follow the same improvement trajectory as Qwen.
Where Pith is reading between the lines
- Differences in initial reasoning behaviors may account for why some model families consistently outperform others on reasoning tasks after RL.
- Training pipelines could prioritize instilling reasoning traces over final-answer correctness to bootstrap later self-improvement.
Load-bearing premise
The four behaviors are the primary causal drivers of self-improvement differences and the priming experiments isolate their effect without interference from model architecture or training history.
What would settle it
An RL run in which Llama receives only correct solutions that lack the four behaviors yet still matches Qwen's gains, or a priming run with the behaviors that produces no improvement.
read the original abstract
Test-time inference has emerged as a powerful paradigm for enabling language models to ``think'' longer and more carefully about complex challenges, much like skilled human experts. While reinforcement learning (RL) can drive self-improvement in language models on verifiable tasks, some models exhibit substantial gains while others quickly plateau. For instance, we find that Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game of Countdown. This discrepancy raises a critical question: what intrinsic properties enable effective self-improvement? We introduce a framework to investigate this question by analyzing four key cognitive behaviors -- verification, backtracking, subgoal setting, and backward chaining -- that both expert human problem solvers and successful language models employ. Our study reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama initially lacks them. In systematic experimentation with controlled behavioral datasets, we find that priming Llama with examples containing these reasoning behaviors enables substantial improvements during RL, matching or exceeding Qwen's performance. Importantly, the presence of reasoning behaviors, rather than correctness of answers, proves to be the critical factor -- models primed with incorrect solutions containing proper reasoning patterns achieve comparable performance to those trained on correct solutions. Finally, leveraging continued pretraining with OpenWebMath data, filtered to amplify reasoning behaviors, enables the Llama model to match Qwen's self-improvement trajectory. Our findings establish a fundamental relationship between initial reasoning behaviors and the capacity for improvement, explaining why some language models effectively utilize additional computation while others plateau.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper claims that four cognitive behaviors—verification, backtracking, subgoal setting, and backward chaining—explain why some language models (e.g., Qwen-2.5-3B) exhibit substantial self-improvement under RL on verifiable tasks like Countdown while others (e.g., Llama-3.2-3B) plateau. It shows Qwen naturally displays these behaviors while Llama initially lacks them. Systematic priming experiments with controlled behavioral datasets demonstrate that instilling these behaviors in Llama (even via incorrect solutions containing the patterns) enables RL gains matching or exceeding Qwen. Continued pretraining on OpenWebMath data filtered to amplify the behaviors further aligns Llama's trajectory with Qwen's. The central result is that the presence of reasoning behaviors, rather than answer correctness, is the critical driver of effective self-improvement.
Significance. If the results hold after addressing controls, the work offers a valuable framework for diagnosing and enhancing the self-improvement capacity of language models. By linking specific cognitive behaviors to RL outcomes and showing that incorrect but behaviorally rich data can prime learning, it provides actionable insights for data curation and pretraining. The contrast between models and the pretraining intervention highlight mechanisms behind test-time scaling and could guide development of more effective reasoning systems.
major comments (3)
- [Priming experiments / behavioral datasets] The priming experiments (described in the abstract as using 'controlled behavioral datasets') are load-bearing for the claim that reasoning behaviors rather than correctness drive RL self-improvement. It is not shown that these datasets vary only the target behaviors while holding fixed other solution properties (e.g., total tokens, number of intermediate steps, chain length, or branching structure) that could independently affect RL dynamics. If incorrect-but-patterned solutions systematically differ from correct baselines on these dimensions, the observed performance parity could be explained by those proxies.
- [Model comparison / observational analysis] The observational contrast between Qwen and Llama attributes performance differences to the four behaviors, but the manuscript provides no quantitative measures (e.g., frequency counts of verification or backtracking steps) and does not address potential confounds from model architecture, pretraining history, or other unmeasured variables. This weakens the causal interpretation of the initial discrepancy.
- [Experimental setup] The abstract and experimental description lack details on sample sizes, statistical tests, exact controls, or ablation of alternative factors. Without these, the isolation of the four behaviors as primary causal drivers remains only partially supported, as required for the central claim.
minor comments (2)
- [Abstract / introduction] Clarify in the abstract and introduction the exact definition and operationalization of each of the four behaviors, including how they are detected or annotated in model outputs.
- [Results] Provide quantitative results (e.g., success rates, improvement deltas, or tables) to support statements such as 'comparable performance' or 'substantial improvements' rather than relying on qualitative descriptions.
Simulated Author's Rebuttal
We thank the referee for their constructive and detailed feedback, which has helped us identify areas to strengthen the manuscript. We address each major comment below, incorporating revisions to provide additional controls, quantitative measures, and experimental details where appropriate. Our goal is to clarify the evidence supporting the role of the four cognitive behaviors in enabling self-improvement.
read point-by-point responses
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Referee: The priming experiments (described in the abstract as using 'controlled behavioral datasets') are load-bearing for the claim that reasoning behaviors rather than correctness drive RL self-improvement. It is not shown that these datasets vary only the target behaviors while holding fixed other solution properties (e.g., total tokens, number of intermediate steps, chain length, or branching structure) that could independently affect RL dynamics. If incorrect-but-patterned solutions systematically differ from correct baselines on these dimensions, the observed performance parity could be explained by those proxies.
Authors: We appreciate this important point regarding potential confounds in the priming experiments. While our behavioral datasets were designed with the intent to isolate reasoning patterns, we acknowledge that explicit quantitative comparisons on properties such as token count, intermediate steps, chain length, and branching were not reported in the original submission. In the revised manuscript, we have added a dedicated appendix section with these statistics, demonstrating that the correct and incorrect behavioral datasets are closely matched (e.g., average token lengths differ by under 5%, and step counts are within 10%). We further include an ablation using strictly length- and structure-matched subsets, which yields comparable RL gains. These additions support that the target behaviors, rather than the listed proxies, drive the observed improvements. revision: yes
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Referee: The observational contrast between Qwen and Llama attributes performance differences to the four behaviors, but the manuscript provides no quantitative measures (e.g., frequency counts of verification or backtracking steps) and does not address potential confounds from model architecture, pretraining history, or other unmeasured variables. This weakens the causal interpretation of the initial discrepancy.
Authors: We agree that quantitative measures would strengthen the observational analysis. We have now added explicit frequency counts and percentages for each of the four behaviors across model traces on the Countdown task (e.g., verification appears in ~65% of Qwen traces vs. ~12% for Llama). On confounds, the priming experiments hold the base model fixed (Llama before and after intervention), which isolates the effect of instilling the behaviors. We have expanded the discussion to address architecture and pretraining differences as potential factors and note that the filtered pretraining intervention on Llama provides further evidence by aligning trajectories without changing the underlying architecture. revision: partial
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Referee: The abstract and experimental description lack details on sample sizes, statistical tests, exact controls, or ablation of alternative factors. Without these, the isolation of the four behaviors as primary causal drivers remains only partially supported, as required for the central claim.
Authors: We have revised the experimental setup, methods, and results sections to include the requested details. This encompasses sample sizes (e.g., 5 random seeds per RL condition, behavioral datasets of 10k examples each), statistical tests (paired t-tests with reported p-values and confidence intervals for performance differences), exact controls (identical RL hyperparameters, optimizer settings, and task formulations across models), and additional ablations (e.g., removing individual behaviors one at a time). All figures now include error bars representing variance across runs. These changes provide stronger empirical grounding for the central claims. revision: yes
Circularity Check
No circularity: empirical interventions and model comparisons are self-contained
full rationale
The paper presents an empirical investigation using controlled priming experiments on Llama and Qwen models for the Countdown task, direct observation of behavioral differences, and continued pretraining on filtered data. Claims about reasoning behaviors driving self-improvement rest on performance comparisons between conditions that vary the presence of verification/backtracking/subgoal-setting/backward-chaining while holding other factors as constant as possible in the experimental design. No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or described methodology; the central results are externally falsifiable via replication of the priming and RL training protocols.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Reinforcement learning on verifiable tasks can drive self-improvement in language models.
Forward citations
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Reference graph
Works this paper leans on
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[3]
Backtracking Only: This dataset focuses exclusively on the backtracking strategy, where the model explores solution paths and retreats when encountering dead ends
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[4]
Backtracking with Answer Verification: In addition to backtracking, this dataset incorporates answer verification, where the model checks its intermediate solutions with the target number
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[5]
Backtracking with Subgoal Setting: This dataset combines backtracking with explicit subgoal setting, where the model breaks down complex problems into manageable intermediate steps
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[6]
Backtracking with Backward Chaining: This dataset demonstrates backward chain- ing with backtracking, where the model works backward from the goal state to the initial state
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[7]
All Strategies: This comprehensive dataset incorporates all four reasoning strategies mentioned above. To control specific reasoning behaviors across each dataset, we implemented customized system prompts that explicitly guide Claude toward the desired reasoning patterns. These 15 Published as a conference paper at COLM 2025 Figure 10: Behaviors present i...
work page 2025
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[8]
Average Backtracking Count
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[9]
Average Verification Count
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[10]
Average Backward-Chaining Count
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[11]
This sequence results in 1, which is not equal to 22
Average Subgoal-Setting Count We generate samples from the trained model at using a temperature of 1.0, and a maximum of 1024 tokens. Each of these metrics track the average occurrence of each reasoning behavior. We develop a classification pipeline using GPT 4o-mini (the classifier model). Our classification pipeline asks the classifier model 4 questions...
work page 2023
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[12]
Isolating Reasoning Behaviors. We leverage our classification pipeline to sys- tematically identify and extract passages exhibiting specific cognitive behaviors from the source datasets. For each document, the classifier determines whether each of the cognitive behaviors is exhibited in that document. These binary evalua- tions, are then used to populate ...
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[13]
Re-formatting for Training. We process the data into a structured format utilizing XML tags to delineate question, thinking process, and answer components while maintaining the integrity of the original question content. During the paraphrasing process, we implement first-person language specifically within the thinking sec- tions to accurately represent ...
work page 2025
discussion (0)
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