Recognition: 3 theorem links
· Lean TheoremOpenThoughts: Data Recipes for Reasoning Models
Pith reviewed 2026-05-12 04:51 UTC · model grok-4.3
The pith
Open data generation recipes train a 7B model to 53 percent on AIME 2025 and 54 percent on GPQA Diamond.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Through systematic investigation of each step in the data generation pipeline with over 1,000 controlled experiments, the authors create the OpenThoughts3 dataset of 1.2 million examples. When this dataset is used to train a 7B model with QwQ-32B as teacher, the resulting OpenThoughts3-7B model reaches 53 percent on AIME 2025, 51 percent on LiveCodeBench covering June 2024 to January 2025, and 54 percent on GPQA Diamond, for respective gains of 15.3, 17.2, and 20.5 percentage points over DeepSeek-R1-Distill-Qwen-7B.
What carries the argument
The data generation pipeline, whose individual stages are isolated and improved through ablation experiments to produce higher-quality reasoning traces.
Load-bearing premise
The large benchmark gains come mainly from the data recipes and pipeline choices rather than from the specific teacher model, base model architecture, or unstated training hyperparameters.
What would settle it
Train an identical 7B model on the OpenThoughts3 data but with a weaker teacher model and measure whether the AIME, LiveCodeBench, and GPQA scores fall back close to the prior open baseline.
read the original abstract
Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often rely on proprietary datasets with little to no public information available. To address this, the goal of the OpenThoughts project is to create open-source datasets for training reasoning models. After initial explorations, our OpenThoughts2-1M dataset led to OpenThinker2-32B, the first model trained on public reasoning data to match DeepSeek-R1-Distill-32B on standard reasoning benchmarks such as AIME and LiveCodeBench. We then improve our dataset further by systematically investigating each step of our data generation pipeline with 1,000+ controlled experiments, which led to OpenThoughts3. Scaling the pipeline to 1.2M examples and using QwQ-32B as teacher yields our OpenThoughts3-7B model, which achieves state-of-the-art results: 53% on AIME 2025, 51% on LiveCodeBench 06/24-01/25, and 54% on GPQA Diamond - improvements of 15.3, 17.2, and 20.5 percentage points compared to the DeepSeek-R1-Distill-Qwen-7B. All of our datasets and models are available on https://openthoughts.ai.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents the OpenThoughts project for developing open-source datasets and pipelines to train reasoning models on math, code, and science tasks. It describes OpenThoughts2-1M yielding OpenThinker2-32B (matching DeepSeek-R1-Distill-32B), followed by systematic investigation of the data generation pipeline via 1,000+ controlled experiments to produce OpenThoughts3. Scaling to 1.2M examples and distilling from QwQ-32B produces OpenThoughts3-7B, which reports SOTA results of 53% on AIME 2025, 51% on LiveCodeBench (06/24-01/25), and 54% on GPQA Diamond—gains of 15.3, 17.2, and 20.5 pp over DeepSeek-R1-Distill-Qwen-7B—with full public release of datasets and models.
Significance. If the performance gains hold after isolating the data recipes from teacher-model effects, this work offers substantial value by releasing large-scale, high-quality open reasoning datasets and models. The scale of controlled pipeline experiments and public artifacts directly address the lack of transparency in proprietary reasoning systems, enabling community replication and extension.
major comments (3)
- [Abstract and results] Abstract and results section: The headline claim attributes the 15–20 pp gains of OpenThoughts3-7B primarily to the data recipes and pipeline choices, yet the final model is distilled from QwQ-32B while the DeepSeek-R1-Distill-Qwen-7B baseline uses a different distillation source. No ablation is reported that holds the teacher model fixed while varying only the data pipeline, leaving the contribution of the recipes entangled with teacher strength and unstated training details.
- [Experiments] Experiments section: The manuscript states that 1,000+ controlled experiments were used to refine the pipeline, but provides insufficient detail on the exact controls (e.g., fixed random seeds, identical base models, statistical significance testing, or handling of confounds such as prompt formatting and filtering thresholds). This weakens the ability to attribute improvements specifically to the reported recipe changes.
- [Results] Results and evaluation: The reported benchmark scores for OpenThoughts3-7B are compared only to DeepSeek-R1-Distill-Qwen-7B; additional baselines using the same teacher (QwQ-32B) or identical base model with prior data recipes are absent, making it hard to confirm that the gains reflect broad reasoning improvements rather than teacher-specific distillation effects.
minor comments (2)
- [Abstract] The abstract and introduction could more explicitly state the base model used for OpenThoughts3-7B and any differences in training hyperparameters relative to the cited DeepSeek baseline.
- [Tables] Tables reporting benchmark results would benefit from inclusion of standard deviations or multiple runs to convey variability, and from clearer labeling of which teacher model was used for each compared entry.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback, which helps clarify the scope of our contributions. We address each major comment below, providing clarifications on our experimental design and noting where revisions will be made to improve transparency. We maintain that the systematic pipeline optimizations offer valuable public insights, while honestly acknowledging limitations in isolating all variables.
read point-by-point responses
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Referee: [Abstract and results] Abstract and results section: The headline claim attributes the 15–20 pp gains of OpenThoughts3-7B primarily to the data recipes and pipeline choices, yet the final model is distilled from QwQ-32B while the DeepSeek-R1-Distill-Qwen-7B baseline uses a different distillation source. No ablation is reported that holds the teacher model fixed while varying only the data pipeline, leaving the contribution of the recipes entangled with teacher strength and unstated training details.
Authors: We acknowledge that the teacher model (QwQ-32B) differs from the one underlying the DeepSeek-R1-Distill-Qwen-7B baseline, and that this entanglement prevents a pure isolation of data recipe effects. Our 1,000+ controlled experiments were conducted with fixed teachers within each ablation to isolate pipeline components such as data filtering, formatting, and example selection. The OpenThoughts2-1M results previously demonstrated that public data recipes can match proprietary distillation performance at the 32B scale. For the 7B model, we selected QwQ-32B as a strong, fully open teacher to maximize performance while keeping the data pipeline public. We will revise the abstract and results to more precisely attribute the gains to the combination of our optimized data recipe and distillation from QwQ-32B, and add an explicit limitations paragraph discussing teacher effects. revision: partial
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Referee: [Experiments] Experiments section: The manuscript states that 1,000+ controlled experiments were used to refine the pipeline, but provides insufficient detail on the exact controls (e.g., fixed random seeds, identical base models, statistical significance testing, or handling of confounds such as prompt formatting and filtering thresholds). This weakens the ability to attribute improvements specifically to the reported recipe changes.
Authors: We agree that additional methodological details are needed for full reproducibility and attribution. The controlled experiments held the base model, teacher, and evaluation setup fixed while varying one pipeline factor at a time (e.g., filtering threshold or prompt template). In the revised manuscript, we will expand the Experiments section with specifics on random seeds, base models used across ablations, statistical testing procedures (including confidence intervals where applicable), and explicit controls for prompt formatting and filtering thresholds. revision: yes
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Referee: [Results] Results and evaluation: The reported benchmark scores for OpenThoughts3-7B are compared only to DeepSeek-R1-Distill-Qwen-7B; additional baselines using the same teacher (QwQ-32B) or identical base model with prior data recipes are absent, making it hard to confirm that the gains reflect broad reasoning improvements rather than teacher-specific distillation effects.
Authors: The primary comparison to DeepSeek-R1-Distill-Qwen-7B follows standard practice for reporting competitive open models. We did not train an additional full-scale model using the prior OpenThoughts2 recipe with QwQ-32B due to the substantial compute required for 1.2M-example distillation. Incremental gains from OpenThoughts2 to OpenThoughts3 were validated at smaller scales through the controlled experiments. We will add a clarifying paragraph in the Results section noting the absence of same-teacher prior-recipe baselines and discussing the potential for teacher-specific effects, while emphasizing that all data and code are released to enable such follow-up work by the community. revision: partial
- A complete same-teacher ablation (training both prior and new data recipes with QwQ-32B at full 1.2M scale) is not feasible within the revision timeline due to computational cost.
Circularity Check
No circularity: purely empirical pipeline with released artifacts
full rationale
The paper reports results from 1000+ controlled experiments on data generation steps, scaling to 1.2M examples, and training OpenThoughts3-7B on public data distilled from QwQ-32B. All claims are direct benchmark measurements (AIME, LiveCodeBench, GPQA) with datasets and models released. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear; the work is self-contained empirical reporting without any reduction of outputs to inputs by construction.
Axiom & Free-Parameter Ledger
Forward citations
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Reference graph
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It involves 30 questions of different levels of difficulty
AIME24: a mathematics competition for high-school students held in 2024. It involves 30 questions of different levels of difficulty. Answers are a single integer from 0 to 999
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It involves 30 questions of different levels of difficulty
AIME25: a mathematics competition for high-school students held in 2025. It involves 30 questions of different levels of difficulty. Answers are a single integer from 0 to 999
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It consists of 40 questions with different difficulty levels
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MATH500: consists of 500 diverse problems in probability, algebra, trigonometry, and geometry. 24 Benchmark Domain / Description Number of Questions Code Generation CodeElo (Quan et al., 2025) Code generation with human-comparable Elo ratings. 391 CodeForces (Penedo et al., 2025) Benchmarking competition-level code generation. 453 LiveCodeBench 05/23-05/2...
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The benchmark measures unit test-based execution accuracy with a difficulty- calibrated Elo rating
CodeElo: consists of 391 real-world programming problems curated from a variety of contests. The benchmark measures unit test-based execution accuracy with a difficulty- calibrated Elo rating
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LiveCodeBench: a benchmark of real-world programming tasks that evaluate a model’s ability to generate, execute, verify, and iteratively repair solutions using unit-test feedback. LiveCodeBench 05/23-05/24 subset has 511 problems released between May 2023 and May 2024, whereas the 06/24-01/25 subset has 369 problems released between May 2024 and Jan. 2025
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GPQA Diamond: a set of 198 challenging questions from the Graduate-Level Google-Proof Q&A Benchmark (GPQA). Questions are in multiple-choice format
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Questions are in multiple-choice and numerical formats
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Questions are in Combinatorics, Number Theory, Algebra, and Geometry
HMMT: 30 questions from the HMMT high school mathematics competition held in February 2025. Questions are in Combinatorics, Number Theory, Algebra, and Geometry
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HLE: a subset of 512 multiple-choice, text-only questions from the Humanity’s Last Exam (HLE) benchmark. F D ECONTAMINATION Contamination with the evaluation datasets is an important issue, since it poses the danger of misleading results over the actual usefulness of the training set. It is expected that training data that contains evaluation questions in...
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We take test sets (MATH500, GPQA Diamond, LiveCodeBench) and sample exact questions from each test set
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Please help me solve this problem:
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We add uncontaminated questions by creating completely original questions manually. Overall, our dataset has 3092 contaminated samples and 3000 uncontaminated samples. We tuned our decontamination algorithm to produce nearly 0 false negatives (marking contaminated questions as decontaminated) while not having many false positives. The results of our final...
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Two of the zeros are additive inverses
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I found a piece of the beginning of the equation and a piece at the end, but the middle was missing
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If a cubic polynomial $\operatorname(p)(\mathbf(x))$ has roots at -1, 2, and 3, and if $\mathfrak(p) (0)=1$ , then the remainder when $\mathfrak(p)(\ensuremath(\mathbf(x)))$ is divided by $\mathbf(X) -1$ is:
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[21]
If 2 is a solution of $x^(3)+h x+10=0$ , then h equals:
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[22]
The number of distinct real solutions of the equation $4x^(3)-8x^(2)+5x-1=0$ is:
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[23]
What is the sum of the squares of the roots of $x^(4)-5x^(2)+6=0$
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[24]
For how many integers $_\mathrm(N)$ is $N^(4)+6N<6N^(3)+N^(2)?$
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[25]
How any times does the graph of $f(x)=x^(3)-x^(2)+2x+4$ cross the $\mathbf(X)$ axis?
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Madison’s dog chewed on her homework before she could finish it. The fragment saved from the horrible canine’s mouth reveal only the two terms of highest degree of the polynomial $\mathfrak(p)(\ ensuremath\mathbf(x)))$ Now please give me your extraction of all text, including text in images. Figure 24: Gemini OCR Prompt 52 You are to reform the following ...
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A spin-half particle is in a linear superposition0.8|\uparrow\rangle+0.6|\downarrow\rangle of its spin -up and spin-down states. If |\uparrow\rangle and |\downarrow\rangle are the eigenstates of \sigma_{ z} , then what is the expectation value up to one decimal place, of the operator 10\sigma_{z}+5\ sigma_{x} ? Here, symbols have their usual meanings
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[30]
Let $S$ be the set of positive integer divisors of $20^9.$ Three numbers are chosen independently and at random with replacement from the set $S$ and labeled $a_1,a_2,$ and $a_3$ in the order they are chosen. The probability that both $a_1$ divides $a_2$ and $a_2$ divides $a_3$ is $\tfrac{m}{n},$ where $m$ and $n$ are relatively prime positive integers. Find $m.$
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[31]
What is the concentration of calcium ions in a solution containing 0.02 M stoichiometric Ca-EDTA complex (we assume that the pH is ideal, T = 25C). KCa-EDTA = 5x10\^10. Negative Questions:
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[32]
Solve 0 = 19 *z - 17 *z for z
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[33]
Simplify ((-2 *(-2*sqrt(1210) - sqrt(1210) - sqrt(20)/sqrt(2) *-6))/((sqrt(1800)*2 + sqrt(1800) + sqrt (1800) + sqrt(1800)) *-1)*3)**2.\n
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