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arxiv: 2606.24855 · v1 · pith:GT37WCDEnew · submitted 2026-06-23 · 💻 cs.AI

OpenThoughts-Agent: Data Recipes for Agentic Models

Negin Raoof , Richard Zhuang , Marianna Nezhurina , Etash Guha , Atula Tejaswi , Ryan Marten , Charlie F. Ruan , Tyler Griggs
show 42 more authors
Alexander Glenn Shaw Hritik Bansal E. Kelly Buchanan Artem Gazizov Reinhard Heckel Chinmay Hegde Sankalp Jajee Daanish Khazi Emmanouil Koukoumidis Xiangyi Li Hange Liu Shlok Natarajan Harsh Raj Nicholas Roberts Ethan Shen Nishad Singhi Michael Siu Ashima Suvarna Hanwen Xing Patrick Yubeaton Robert Zhang Leon Liangyu Chen Xiaokun Chen Steven Dillmann Saadia Gabriel Xunyi Jiang Anurag Kashyap Boxuan Li Yein Park Minh Pham Sujay Sanghavi Lin Shi Ke Sun Yixin Wang Zhiwei Xu Erica Zhang Siyan Zhao Wanjia Zhao Jenia Jitsev Alex Dimakis Benjamin Feuer Ludwig Schmidt
This is my paper

Pith reviewed 2026-06-25 22:59 UTC · model grok-4.3

classification 💻 cs.AI
keywords agentic modelsdata curation pipelinefine-tuningagent benchmarksopen training datatask diversityscaling properties
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The pith

An open data curation pipeline for agentic models produces a 100K-example training set that lifts fine-tuned 32B accuracy to 44.8 percent across seven benchmarks.

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

The paper sets out to build and validate a fully open pipeline for assembling training data that supports agentic language models across varied tasks rather than single benchmarks. Over 100 controlled ablations reveal that task source diversity drives better generalization. The resulting 100K-example dataset, when used to fine-tune Qwen3-32B, reaches 44.8 percent average accuracy and exceeds the prior strongest open-data model by 3.9 points. The work matters because public recipes for agent data have been scarce, leaving most progress dependent on closed collections. If the pipeline holds, it supplies a reproducible route to stronger, more transferable agents without proprietary resources.

Core claim

The OpenThoughts-Agent pipeline systematically varies task sources and diversity levels, then assembles 100K training examples that, after fine-tuning on Qwen3-32B, deliver 44.8 percent average accuracy on seven agentic benchmarks and a 3.9-point gain over Nemotron-Terminal-32B while also showing superior scaling behavior at every dataset size.

What carries the argument

The OT-Agent data curation pipeline, which selects and balances examples from multiple task sources after ablation-driven tuning of diversity and quality filters.

If this is right

  • Training sets built with the same pipeline will continue to outperform alternative open collections at every scale.
  • The released 100K examples and pipeline code enable direct replication and further scaling experiments.
  • Insights from the ablations on task sources can be reused to curate data for additional agent domains.
  • Models trained this way exhibit measurable transfer across the tested benchmarks rather than overfitting to one.

Where Pith is reading between the lines

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

  • The same curation logic could be applied to smaller or larger base models to test whether the diversity benefit persists.
  • Public release of the full experimental logs allows others to identify which task sources contributed most to the observed gains.
  • If real deployments involve tool-use patterns absent from the seven benchmarks, additional targeted sources may still be needed.

Load-bearing premise

The seven chosen agentic benchmarks are representative enough of the broader space of agent tasks that gains on them will appear on new, unseen workloads.

What would settle it

Run the released fine-tuned model on a fresh agentic benchmark outside the original seven and observe no accuracy improvement relative to the prior open baseline.

Figures

Figures reproduced from arXiv: 2606.24855 by Alexander Glenn Shaw, Alex Dimakis, Anurag Kashyap, Artem Gazizov, Ashima Suvarna, Atula Tejaswi, Benjamin Feuer, Boxuan Li, Charlie F. Ruan, Chinmay Hegde, Daanish Khazi, E. Kelly Buchanan, Emmanouil Koukoumidis, Erica Zhang, Etash Guha, Ethan Shen, Hange Liu, Hanwen Xing, Harsh Raj, Hritik Bansal, Jenia Jitsev, Ke Sun, Leon Liangyu Chen, Lin Shi, Ludwig Schmidt, Marianna Nezhurina, Michael Siu, Minh Pham, Negin Raoof, Nicholas Roberts, Nishad Singhi, Patrick Yubeaton, Reinhard Heckel, Richard Zhuang, Robert Zhang, Ryan Marten, Saadia Gabriel, Sankalp Jajee, Shlok Natarajan, Siyan Zhao, Steven Dillmann, Sujay Sanghavi, Tyler Griggs, Wanjia Zhao, Xiangyi Li, Xiaokun Chen, Xunyi Jiang, Yein Park, Yixin Wang, Zhiwei Xu.

Figure 1
Figure 1. Figure 1: The OpenThoughts-Agent-SFT dataset leads to SotA performance on Terminal-Bench 2.0 and an 100-subset of SWE-Bench Verified at large dataset scales. The all-benchmark average is over the seven benchmarks reported in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Six-stage SFT data pipeline for OpenThoughts-Agent. Each stage is ablated indepen￾dently in Sections 3.1–3.6. effective post-training of AI agents, spanning both SFT and RL data curation [47, 13, 35, 38, 49, 27, 8]. However, these works have two key limitations; firstly, they almost exclusively focus either on SFT or on RL, with little attention paid to how these steps intersect; secondly, they tend to foc… view at source ↗
Figure 3
Figure 3. Figure 3: Synthetic augmentation scales past the upsampling plateau. Both methods build on the same 10K base and diverge only when scaling beyond it. Method 1 (upsampling additional rollouts per task description) plateaus from 31.6K to 100K, while Method 3 (synthetic task augmentation) continues to improve on all three benchmarks. Error bars are standard error across three stochastic re-runs. Source Mix SWE-Bench Ve… view at source ↗
Figure 4
Figure 4. Figure 4: OpenThoughts-Agent Full Data Pipeline. Our final SFT dataset is 100k agentic traces. Benchmarks Average Rank RL Data Source SWE-bench Verified (100) OT-TBLite Terminal-Bench 2.0 Raw Normalized 1 pymethods2test 35.67 1.83 16.02 1.58 13.48 1.50 21.72 +1.73 2 r2egym 28.67 1.67 16.84 1.64 6.74 1.45 17.42 +0.50 3 nemotron-code-oracle 25.00 1.86 16.78 1.67 6.74 1.24 16.17 +0.22 4 llm-verifier-freelancer 22.33 1.… view at source ↗
Figure 5
Figure 5. Figure 5: The OpenThoughts-Agent data recipe scales at 8B. SWE-bench Verified-100 and Terminal-Bench 2.0 accuracy for Qwen3-8B fine-tuned on OpenThoughts-Agent-v2 across dataset sizes, compared against the Nemotron-Terminal-Corpus baseline and the base Qwen3-8B model (dashed). OpenThoughts-Agent leads at the larger scales and surpasses the baseline on both bench￾marks at 100K. Error bars denote standard error across… view at source ↗
Figure 6
Figure 6. Figure 6: Hero (pymethods2test) RL-time reward peaks and then collapses. Mean reward per rollout in 4-hour wall-clock bins (blue line) over the hero run, with the post-RL and pre-RL eval-checkpoint mean rewards on held-out SWE-Bench-Verified marked on the right (diamond = post-RL, square = pre-RL base). Reward rises modestly to a peak near 0.51, then collapses to ≈ 0.13 as the policy over-explores (mean turns and th… view at source ↗
Figure 7
Figure 7. Figure 7: Baseline (llm-verifier-freelancer) RL-time reward rises near-monotonically. Mean reward per rollout in 4-hour bins (blue line, clustered at right of the wall-clock axis because the pre-RL base eval – gray square – predates training by months) rises smoothly from ≈ 0.54 to ≈ 0.73 with no collapse, the mirror image of [PITH_FULL_IMAGE:figures/full_fig_p032_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Time-binned behavioral dynamics of the hero (pymethods2test) run. Reward, error/parse-error/premature-stop rates, and nine per-trace behavioral features over the run (4-hour bins on a shared RL-time axis). The exploration-then-collapse signature is visible across panels: as reward plateaus and then falls, mean conversation turns, mean tokens per assistant message, think tokens per trace, and self-correctio… view at source ↗
read the original abstract

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project addresses this gap with a fully open data curation pipeline for training agentic models. We conduct more than 100 controlled ablation experiments to systematically investigate each stage of the pipeline, yielding insights on the importance of task sources and diversity. We then assemble a training set of 100K examples from our pipeline and fine-tune Qwen3-32B on this dataset, which yields an average accuracy of 44.8% across seven agentic benchmarks and a 3.9 percentage point improvement over the strongest existing open data agentic model (Nemotron-Terminal-32B, 40.9%). Moreover, our training data exhibits strong scaling properties, outperforming alternative open datasets at every training set size in compute-controlled comparisons. We publicly release our training sets, data pipeline, experimental data, and models at openthoughts.ai to support future open research on agentic model training.

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

3 major / 2 minor

Summary. The paper introduces OpenThoughts-Agent, a fully open data curation pipeline for training agentic language models. It reports over 100 controlled ablation experiments on task sources and diversity, assembles a 100K-example training set, and fine-tunes Qwen3-32B to achieve 44.8% average accuracy across seven agentic benchmarks (a 3.9pp gain over Nemotron-Terminal-32B at 40.9%). The work also claims strong scaling properties relative to alternative open datasets and publicly releases the training sets, pipeline, experimental data, and models.

Significance. If the empirical results hold under scrutiny, the work offers a practical, reproducible recipe for curating agentic training data and provides systematic ablation insights into the roles of task sources and diversity. The public release of all artifacts is a clear strength that directly supports community follow-up and verification. The scaling comparisons and multi-benchmark gains, if statistically robust, would be useful for practitioners building open agentic models.

major comments (3)
  1. [Abstract / Evaluation] Abstract and evaluation protocol: The central generalization claim—that the pipeline produces models that 'generalize across diverse agentic tasks'—rests on performance across seven benchmarks, yet the manuscript provides no external validation set, out-of-distribution agentic workloads, or analysis of potential overlap between the 100K training examples and the evaluation benchmarks. Without such checks, the 3.9pp improvement could reflect benchmark-specific effects rather than broad capability.
  2. [Abstract / Ablations] Ablation experiments (abstract): The paper states that more than 100 controlled ablations were performed to investigate task sources and diversity, but supplies no details on statistical significance testing, error bars, run-to-run variance, or precise data-exclusion rules. These omissions make it impossible to assess whether the reported insights on pipeline stages are reliable or merely suggestive.
  3. [Abstract / Scaling] Scaling properties claim (abstract): The assertion that the training data 'exhibits strong scaling properties, outperforming alternative open datasets at every training set size in compute-controlled comparisons' is load-bearing for the data-recipe contribution, yet the manuscript does not specify the exact compute controls, the alternative datasets used, or the functional form of the scaling curves.
minor comments (2)
  1. [Abstract] The abstract refers to 'seven agentic benchmarks' without naming them or providing a table of per-benchmark scores; adding this information would improve clarity.
  2. [Abstract] Notation for model names (e.g., Qwen3-32B, Nemotron-Terminal-32B) should be defined consistently on first use.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments identify important areas for strengthening the claims around generalization, ablation reliability, and scaling comparisons. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and evaluation protocol: The central generalization claim—that the pipeline produces models that 'generalize across diverse agentic tasks'—rests on performance across seven benchmarks, yet the manuscript provides no external validation set, out-of-distribution agentic workloads, or analysis of potential overlap between the 100K training examples and the evaluation benchmarks. Without such checks, the 3.9pp improvement could reflect benchmark-specific effects rather than broad capability.

    Authors: We agree that explicit checks for overlap and out-of-distribution performance would better support the generalization claim. The seven benchmarks were selected to span distinct agentic domains (e.g., code editing, terminal use, web navigation), and the 3.9pp gain is measured against a baseline trained on the same evaluation suite. In the revision we will add an n-gram and embedding-based overlap analysis between the 100K training set and each benchmark, plus results on at least one additional held-out agentic workload if a suitable public one can be identified. This addresses the concern directly without altering the core empirical results. revision: yes

  2. Referee: [Abstract / Ablations] Ablation experiments (abstract): The paper states that more than 100 controlled ablations were performed to investigate task sources and diversity, but supplies no details on statistical significance testing, error bars, run-to-run variance, or precise data-exclusion rules. These omissions make it impossible to assess whether the reported insights on pipeline stages are reliable or merely suggestive.

    Authors: The 100+ ablations followed a controlled design varying one factor at a time while holding others fixed, but we omitted variance estimates and exclusion criteria in the initial draft. We will revise the relevant sections to report standard deviations from repeated runs (where compute permitted), describe the exact data-exclusion heuristics applied at each pipeline stage, and note any cases where single-run results are presented. These additions will make the reliability of the task-source and diversity insights clearer. revision: yes

  3. Referee: [Abstract / Scaling] Scaling properties claim (abstract): The assertion that the training data 'exhibits strong scaling properties, outperforming alternative open datasets at every training set size in compute-controlled comparisons' is load-bearing for the data-recipe contribution, yet the manuscript does not specify the exact compute controls, the alternative datasets used, or the functional form of the scaling curves.

    Authors: The scaling experiments matched total training tokens across datasets and used the same base model and optimizer settings. Alternative datasets were SWE-Smith, SERA, and Nemotron-Terminal subsets. Curves were plotted as accuracy versus log(training-set size). We will expand the scaling section to state these controls explicitly, enumerate the comparison datasets, and specify the functional form (log-linear fits with reported R^{2} values). This will make the 'strong scaling properties' claim fully reproducible from the text. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results on held-out benchmarks

full rationale

The paper describes an empirical data curation pipeline producing 100K examples, supported by >100 internal ablations on task sources and diversity. The headline result (44.8% average accuracy on seven agentic benchmarks after fine-tuning Qwen3-32B) is obtained by direct evaluation on separate benchmarks, with no equations, fitted parameters renamed as predictions, or self-citations invoked to derive the metric. Performance is measured externally rather than reduced to inputs by construction. No self-definitional, uniqueness, or ansatz patterns appear. This is a standard empirical ML study whose central claims remain independent of the reported numbers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review based only on abstract; no explicit free parameters, axioms, or invented entities are stated. The central claim rests on the unstated premise that benchmark accuracy on the seven tasks measures useful agentic capability.

axioms (1)
  • domain assumption The seven agentic benchmarks adequately sample the space of tasks that matter for generalization.
    Abstract invokes this when claiming the pipeline yields models that generalize across diverse agentic tasks.

pith-pipeline@v0.9.1-grok · 5970 in / 1295 out tokens · 15476 ms · 2026-06-25T22:59:08.866764+00:00 · methodology

discussion (0)

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