High-quality reasoning traces are rare at scale; this cleaned corpus focuses on making Claude Opus 4.7–derived reasoning examples reliable for distillation and evaluation. By removing simulated “thinking,” exact-duplicate prompts, and rows with missing fields, the dataset surfaces clearer input→response pairs suitable for supervised fine-tuning and benchmarking.
What Sets It Apart
- Focused cleaning for reasoning fidelity: the maintainer removed responses where the assistant “simulates” internal reasoning (e.g., long meta-descriptions like “Now I'm laying out the puzzle grids...”); 1,230 such rows were dropped. This reduces agentic/hallucinatory artifacts that harm downstream training.
- Deduplication and quality filtering: exact-prompt duplicates (989 rows) and entries missing core fields (1,098 rows) were removed, leaving 4,807 rows from an original 8,124 (59.2% retention). That balance keeps diverse examples while eliminating overt noise.
- Straight JSON chat format: each row keeps a messages array (system/user/assistant) plus explicit
thinkingandresponsefields, matching common chat-centric fine-tuning pipelines and easy ingestion with pandas/polars.
Who It's For (and trade-offs)
Great fit if you need a compact, cleaned corpus of LLM reasoning examples for distillation, supervised fine-tuning, or targeted evaluation of step-by-step problem solving. It’s especially useful for researchers wanting to avoid agentic “thinking” artifacts when training models to produce concise reasoning traces. Look elsewhere if you require the original, unfiltered provenance (the dataset references an upstream original for full collection details) or if you need much larger-scale datasets — this cleaned variant prioritizes fidelity over raw size.
Where It Fits
Use this as a mid-sized, high-signal supplement when distilling reasoning behavior from Claude Opus 4.7 into another model or when building evaluation suites that penalize simulated internal monologue. The packaging is Apache-2.0, but note the content is subject to Anthropic’s usage policies noted by the upstream source.
