Learning Optimal Agent Behavior From a Synthetic Reasoning Action Dataset
Bibliographic record
Abstract
Recent advances in large language models (LLMs) have demonstrated impressive capabilities in reasoning and problem solving. However, a key limitation persists: most models rely on static, pre-injected external knowledge provided at the beginning of generation, without the ability to dynamically retrieve or act upon information mid-process. This fixed context window approach often leads to hallucinations, outdated references, or incomplete answers, particularly in complex or open-ended tasks. To address this, we propose integrating action calls such as function executions, API queries, or web searches during the reasoning process, allowing the model to access and incorporate relevant external information at decision-critical moments. This mechanism more closely resembles human reasoning: when solving a problem, humans often recognize the need for additional information, consult a resource (e.g., a search engine or document), and then refine their conclusions based on the new input. By enabling models to interleave reasoning with tool use, we can significantly enhance their accuracy, factual grounding, and adaptability. This approach is particularly valuable in the context of LLM-based agents, which must operate autonomously in dynamic environments. In such settings, the ability to perform contextual information retrieval or computation mid-generation is essential for robust, real-time decision making. Compared to static knowledge injection, action-in-the-loop reasoning empowers agents to ask the right questions at the right time, thereby reducing uncertainty and improving overall performance across a wide range of tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".