Datasets:

domyn
/

Domyn-Edge-Teacher-Code-Mix

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
pretty_name: Domyn Edge Teacher Code Mix
size_categories:
  - 1M<n<10M
tags:
  - domyn
  - domyn-edge
  - teacher
  - code
  - grpo
configs:
  - config_name: sft
    data_files:
      - split: train
        path: data/sft/train-*.parquet
      - split: test
        path: data/sft/test-*.parquet
  - config_name: rl
    data_files:
      - split: train
        path: data/rl/train-*.parquet
      - split: test
        path: data/rl/test-*.parquet

Domyn Edge Teacher Code Mix

Phased release. Domyn Edge is a rolling release in three bundles. This dataset is part of the second bundle, together with the domain teachers and their post-training data. For an overview, see the launch blog post.

The training data of Domyn Edge Teacher Code, in two configs:

Config	Stage	Train	Test
`sft`	Supervised fine-tuning, 64k context	2,793,453 (38.48B tokens)	147,130 (2.03B tokens)
`rl`	GRPO, unit-tested programming problems	30,910 (19.90M tokens)	1,069 (0.65M tokens)

`sft`: supervised fine-tuning mix

The code and software-engineering slices of the Domyn Edge SFT corpus. Tool-use data belongs to the agentic teacher and math to the math teacher, so both have weight 0. Conversations longer than 65,536 tokens were dropped. 5% of the mix is held out as the test split. Conversations whose prompts overlap with the final evaluation benchmarks are excluded, to keep the mix usable for evaluation.

Task	Weight	Sources
Code	1.0	Nemotron-SFT-Competitive-Programming-v2, INTELLECT-3-SFT (`openreasoning_code`), Dolci-Think-SFT-7B, Nemotron-SFT-CUDA-v1
Software engineering	1.0	Nemotron-SFT-SWE-v2, Nemotron-SFT-SWE-v3, INTELLECT-3-SFT (`swe_swiss`)

Released conversations and tokens per source, train and test splits together (licenses in short; full terms in ATTRIBUTION.md):

Source	License	Conversations	Tokens
INTELLECT-3-SFT	CC BY 4.0, MIT, Apache 2.0 (per subset)	1,448,126	19.52B
Nemotron-SFT-Competitive-Programming-v2	CC BY 4.0, ODC-BY 1.0	692,261	14.01B
Dolci-Think-SFT-7B	ODC-BY 1.0	540,841	3.49B
Nemotron-SFT-SWE-v2	CC BY 4.0	251,494	3.33B
Nemotron-SFT-SWE-v3	CC BY 4.0	5,643	142.0M
Nemotron-SFT-CUDA-v1	CC BY 4.0	2,218	21.4M
Total		2,940,583	40.51B

`rl`: GRPO problems

Programming problems with unit tests. The reward is the fraction of tests the program in the response passes, run in a sandbox. Each prompt carries a system message that asks for the final solution in a single Python code block. Problems that overlap with the final evaluation benchmarks are excluded, to keep the config usable for evaluation.

Source	License	Train rows	Train tokens	Test rows	Test tokens
DeepCoder-Preview-Dataset	MIT	20,352	11.84M	939	0.55M
Nemotron-3-Nano-RL-Training-Blend, code slice	CC BY-SA 4.0	10,558	8.06M	130	0.10M
Total		30,910	19.90M	1,069	0.65M

Tokens are prompt tokens, as in the token_length column. For DeepCoder rows, metadata.subset names the original collection (for example codeforces).

How to use

from datasets import load_dataset

sft = load_dataset("domyn/Domyn-Edge-Teacher-Code-Mix", "sft", split="train", streaming=True)
rl = load_dataset("domyn/Domyn-Edge-Teacher-Code-Mix", "rl", split="train")

Columns

`sft` columns

Column	Type	Description
`messages`	list	The conversation. Each message has `role`, `content`, `reasoning_content` (assistant reasoning, if any), `tool_calls` (each with `id`, `type` and `function` {`name`, `arguments` as a JSON string}), and `name` and `tool_call_id` for tool results.
`tools`	string	JSON-encoded list of the tool schemas available in the conversation, or null.
`task`	string	Task family.
`source`	string	Upstream dataset.
`license`	string	License of the upstream record.
`metadata`	string	JSON-encoded upstream metadata (for example topic or generation model), or null.
`uuid`	string	Record identifier.

`rl` columns

The rl rows use the same canonical form as the sft rows, plus the reward data and the prompt length.

Column	Type	Description
`messages`	list	The prompt: `role` and `content` of each message (the other message fields are null).
`tools`	string	Always null in this config.
`task`	string	Task family.
`source`	string	Upstream dataset.
`license`	string	License of the upstream record.
`metadata`	string	JSON-encoded upstream metadata.
`reward`	string	JSON-encoded reward data: `ground_truth` and the scoring `style`. Here `ground_truth.tests` is a JSON-encoded object with the test `inputs` and `outputs` and, for function-style problems, `fn_name`.
`token_length`	int	Prompt length in tokens.
`uuid`	string	Record identifier.

To train with NeMo-Gym, as we did, map each row to the code_gen_simple_agent input: responses_create_params.input = messages; verifier_metadata.unit_tests = the decoded reward.ground_truth.tests; agent_ref = {"type": "responses_api_agents", "name": "code_gen_simple_agent"}.

License

Our contributions to this dataset, including the curation and the mix composition, are released under the Apache License 2.0. Upstream datasets keep their original license terms. Read ATTRIBUTION.md before redistributing any part of this data.

Citation

@misc{domyn_edge_teacher_code_mix_2026,
  title     = {Domyn Edge Teacher Code Mix},
  author    = {Domyn},
  year      = {2026},
  note      = {Technical report, forthcoming in November 2026.},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/domyn/Domyn-Edge-Teacher-Code-Mix}
}

Domyn Edge Teacher Code Mix

sft: supervised fine-tuning mix

rl: GRPO problems