Why the reasoning trace is the dataset

The inversion

Most open reasoning datasets treat chain-of-thought as incidental output: reasoning text that happens to precede an answer. AstraGPT Reasoning Dataset v1 is built the other way around. The trace is the artifact; the answer is what the trace commits to.

Every one of the 193,841 outputs begins with a delimited Attempts Blocks section — exploration, dead ends, self-correction, confidence — before the final answer.

One asset, three research agendas

• Generation: supervised fine-tuning on the trace teaches the model to produce reasoning, not just answers.
• Verification: a small head over the trace's hidden states can learn to predict factuality before the answer exists.
• Uncertainty routing: trace shape — length, revision count, hedging — becomes a token-level signal of how hard the model found the problem.

The third point is the interesting one for sparse models. If router uncertainty correlates with trace effort, routing decisions get a ground-truth proxy they previously lacked.

Schema and why it is boring on purpose

Four columns. No metadata sprawl. The structure lives inside the output string, fenced so it can be masked, extracted or loss-weighted independently of the answer.

What it is not

• Not a benchmark: high-quality traces do not guarantee correct answers.
• Not PII-free by construction: incidental PII in sourced prompts can be reported and is scrubbed in the next release.
• Not a replacement for human evaluation on safety-critical tasks.

Apache-2.0, open to everyone. The corpus was used to train AstraGPT-7B and AstraGPTCoder-7B.