Opus-4.6-Reasoning-2160x

# Opus-4.6-Reasoning-2160x **2,160 high-quality reasoning traces** generated by Claude Opus 4.6 via OpenRouter, covering mathematics, competitive programming, logic, science, and language tasks. Each example includes the full problem, an extended chain-of-thought, and a final solution — making the dataset suitable for supervised fine-tuning, chain-of-thought distillation, and reasoning-capability transfer to smaller models. Originally generated as a batch of 3,305 examples; 1,145 were removed during quality filtering (refusals, empty responses, truncated problems). The 2,160 remaining examples are all substantive, complete reasoning demonstrations. --- ## Dataset at a Glance | Property | Value | |---|---| | Rows | 2,160 | | Avg tokens per row | ~758 | | Total completion tokens | 1,636,368 | | Difficulty classes | easy / medium / hard | | Category classes | 2 (math/reasoning, code/logic) | | License | Apache 2.0 | | Generation cost | ~$409 USD at Opus 4.6 pricing | --- ## Schema ```jsonl { "id": "drive_minimax_m2.1_questions_70109", "problem": "Your solution must read input from standard input...", "thinking": "Looking at what was provided, this appears to be...", "solution": "The problem statement appears to be incomplete...", "difficulty": "medium", "category": "code", "timestamp": "2026-02-12T21:49:00Z", "hash": "a3f9c1d2e4b78901" } ``` | Field | Type | Description | |---|---|---| | `id` | string (5–36 chars) | Unique example identifier | | `problem` | string (5–13.7k chars) | The problem or prompt given to the model | | `thinking` | string (80–13.8k chars) | Opus 4.6's full chain-of-thought reasoning | | `solution` | string (20–15.1k chars) | Final answer or solution | | `difficulty` | string (3 values) | `easy`, `medium`, or `hard` | | `category` | string (2 values) | High-level domain label | | `timestamp` | string (32 chars) | ISO 8601 generation timestamp | | `hash` | string (16 chars) | Deduplication hash | --- ## Loading the Dataset ```python from datasets import load_dataset ds = load_dataset("CompactAI-O/Opus-4.6-Reasoning-2160x", split="train") print(ds[0]["problem"]) print(ds[0]["thinking"]) print(ds[0]["solution"]) ``` --- ## Training Use Cases This dataset is designed for **knowledge transfer from a frontier reasoning model to smaller, more efficient models**. Below are the primary training patterns. ### 1. Standard Supervised Fine-Tuning (SFT) Use `problem` → `solution` for instruction-following training. The solution fields are clean, complete answers suitable for direct target supervision. ```python def format_sft(row): return { "input": row["problem"], "output": row["solution"] } ``` Best for: models already capable of basic instruction following that need domain knowledge transfer. ### 2. Chain-of-Thought Distillation Train on `problem` → `thinking + solution` to transfer Opus 4.6's reasoning style. The `thinking` field contains multi-step reasoning that is not present in shorter/weaker model outputs. ```python def format_cot(row): return { "input": row["problem"], "output": f"{row['thinking']}\n\n{row['solution']}" } ``` Best for: models being trained to produce explicit reasoning before answering (Qwen, Phi, Mistral, FANT-class architectures with `<|think|>` tokens). ### 3. Think-Solution Format (FANT3 / structured reasoning) For architectures that use explicit thinking delimiters (e.g. `<|think|>...<|answer|>...`): ```python def format_think_answer(row, think_open="<|think|>", think_close="<|/think|>", ans_open="<|answer|>", ans_close="<|/answer|>"): return ( f"{think_open}{row['thinking']}{think_close}" f"{ans_open}{row['solution']}{ans_close}" ) ``` This format is used by FANT3 and similar models that separate latent reasoning from final output at the token level. ### 4. Difficulty-Weighted Sampling The `difficulty` field allows curriculum learning — train on `easy` first, then progressively introduce `medium` and `hard`. ```python from datasets import load_dataset ds = load_dataset("CompactAI-O/Opus-4.6-Reasoning-2160x", split="train") easy = ds.filter(lambda x: x["difficulty"] == "easy") medium = ds.filter(lambda x: x["difficulty"] == "medium") hard = ds.filter(lambda x: x["difficulty"] == "hard") ``` ### 5. Category-Filtered Training Use `category` to mix domain-specific signals with general corpora: ```python math_ds = ds.filter(lambda x: x["category"] == "math") code_ds = ds.filter(lambda x: x["category"] == "code") ``` ### 6. Sequence-Level KD (Knowledge Distillation) When used alongside a local teacher model, this dataset supports GKD-style sequence-level distillation. The `thinking` + `solution` sequences serve as teacher samples. No logit alignment is needed — this is pure sequence supervision. ```python # Example with TRL SFTTrainer from trl import SFTTrainer, SFTConfig trainer = SFTTrainer( model=student_model, train_dataset=ds, args=SFTConfig( output_dir="./output", max_seq_length=2048, ), formatting_func=format_cot, ) trainer.train() ``` --- ## Problem Coverage Examples span a wide range of domains: - **Mathematics**: algebra, geometry, arithmetic word problems, combinatorics - **Competitive programming**: string manipulation, graph algorithms, dynamic programming - **Logic & NLI**: premise/hypothesis entailment, paraphrase detection - **Science**: chemistry, biology, physics - **Language tasks**: translation, reading comprehension, extraction - **General QA**: history, geography, factual recall --- ## Cleaning Report | Rejection reason | Count | % of original | |---|---|---| | Refusal / incomplete problem | 1,090 | 33.0% | | Empty response | 18 | 0.5% | | Response had no substance | 17 | 0.5% | | Problem too short | 14 | 0.4% | | Response too short | 6 | 0.2% | | **Total removed** | **1,145** | **34.6%** | | **Total kept** | **2,160** | **65.4%** | Cleaning was performed on 2026-02-12. All kept examples have a substantive problem statement and a non-trivial Opus 4.6 response. --- ## Decontamination Before using this dataset in any evaluation context, apply n-gram decontamination against your eval sets (GSM8K, MATH-500, MMLU). The NuminaMath and similar math-adjacent sources in the original generation prompts carry a small overlap risk. Verbatim contamination rate against standard benchmarks is estimated at <0.5%. --- ## Generation Details - **Model**: Claude Opus 4.6 via OpenRouter - **Pricing at generation time**: $5.00/M input tokens, $25.00/M output tokens - **Total output tokens**: 1,636,368 - **Estimated cost**: ~$409 USD - **Average turns**: 1.00 (single-turn, no tool calls) - **Generation date**: February 2026 --- ## Citation If you use this dataset, please credit the original generation effort: ``` @misc{crownelius2026opus46reasoning, title = {Opus-4.6-Reasoning-2160x}, author = {Crownelius / CompactAI-O}, year = {2026}, url = {https://huggingface.co/datasets/CompactAI-O/Opus-4.6-Reasoning-2160x} } ``` --- ## License Apache 2.0. Generated outputs from Claude Opus 4.6 are subject to Anthropic's usage policies. Commercial use of distilled model outputs should comply with Anthropic's terms of service.