geodesic-research/pa-warm-start-sft-medium-5b-mix

Auto-generated by dataset-builder. Each config below is a separate dataset produced from a versioned YAML build config. Load with:

from datasets import load_dataset

ds = load_dataset("geodesic-research/pa-warm-start-sft-medium-5b-mix", "<config_name>", revision="<commit-sha>")

Pin revision= to the specific commit SHA you want; without it, you get the current HEAD of the dataset repo, which may change when the builder re-pushes.

Requires datasets v4+. These parquet files carry feature metadata written by datasets 4.x, which uses type names ({"_type": "List"}) that 3.x does not recognise — a 3.x load_dataset fails with a bare must be called with a dataclass type or instance, naming neither the file nor the cause. The Arrow data itself is fine, so a consumer stuck on 3.x can read the parquet through Arrow directly and let the schema be inferred — but inference recovers the values, not always the declared features (an all-empty list column infers as null), so check the columns you care about rather than assuming a clean round-trip.

Composition (default config)

The 5.00B-token "Medium" warm-start SFT mix: safety-free maths / coding / science / chat / agentic tool use, sampled toward the shortest chains of thought, with every document under 32,768 tokens so nothing truncates at the training sequence length. Shuffled (seed 42). Built by pipelines/persistent-alignment-warm-start/ (mix: medium) in dataset-builder; each per-source config on this repo is one collect stage's output and default is their shuffled concatenation.

It is the Light mix's purpose at five times the scale — teach a base checkpoint to reason inside <think></think>, use tools, and stop when expected, carrying as little inherited behaviour as possible — holding the same 25% tool-use share, which validate_mix enforces at config load so a budget edit cannot silently drift it.

1.25B of the mix (25.00%) is tool-use data, across six configs: opencode, arc_agi_tools, agentic_interactive, swe_r2e_gym, science_rqa_tools, and swe_opencode_harness. Four of those sources are not read by the Light mix; they are here for breadth of tool vocabulary — an arm drawn overwhelmingly from one corpus teaches that corpus's tool conventions rather than tool use in general. In every tool-using document, every tool the conversation calls is declared in that document's own tools schema, every tool call's arguments encode a JSON object, and tool_calls / tools are stored as structured objects rather than JSON-encoded strings.

Reasoning convention. Every assistant turn in a tool-free document carries a reasoning trace and answers. The six tool-bearing configs keep the tool-chain exemption: an assistant turn that only issues the next call in an ongoing tool chain need not carry a separate trace, which is what admits real agentic trajectories — the Light mix, by contrast, holds the strict form everywhere. Per-record columns cot_lengths_chars, cot_chars_mean / cot_chars_min / cot_chars_max, and n_tokens support analysis of the CoT-length distribution.

No model or vendor identity. A document is dropped outright if a case-insensitive word-boundary match for any of 24 AI model/organisation names (OpenAI, ChatGPT, Claude, Anthropic, DeepSeek, Gemini, Llama, Qwen, Mistral, Copilot — and NVIDIA/Nemotron, the upstream vendor) appears anywhere in the rendered document, reasoning traces included.

System prompts are sampled, not fixed. A document that arrived without standing instructions gets one drawn uniformly from a four-entry pool of deliberately bare prompts ("You are an AI assistant." and three variants), ~29% are deliberately left with none (an empty system block) so the no-prompt condition stays in distribution, and documents carrying real instructions from their source keep them. system_prompt_id records each record's origin: 0–3 a pool entry, -1 deliberately none, -2 the source's own system/developer turn or a role stated in its opening user turn, -3 a preamble hoisted out of that turn. The no-prompt condition is system_prompt_id == -1, not < 0.

No VEA screening. The Light mix screens reasoning traces for verbalised evaluation awareness with a regex router + LLM judge; this mix does not — there is no vea_judge / vea_excluded config here.

source	documents	tokens	longest doc (tokens)	mean CoT (chars)	longest CoT (chars)
nvidia/Nemotron-SFT-Science-v2::so	1,499,287	1,009,137,640	3,899	2,095	5,446
nvidia/Nemotron-SFT-Math-v4::train	201,857	902,934,804	29,029	10,579	38,065
nvidia/Nemotron-SFT-Science-v2::rqa_plain	244,949	750,773,699	17,055	7,181	20,134
nvidia/Nemotron-SFT-Science-v2::syn_mcq	78,349	48,509,165	3,326	1,635	4,957
nvidia/Nemotron-SFT-Instruction-Following-Chat-v3::instruction_following	85,253	186,921,531	29,476	843	2,072
nvidia/Nemotron-SFT-OpenCode-v1::general	37,446	572,014,499	32,746	0	0
nvidia/Nemotron-SFT-SWE-v2::agentless	86,707	420,368,440	32,586	9,125	16,137
nvidia/Nemotron-SFT-Competitive-Programming-v2::python_01	37,773	266,129,182	29,387	21,188	53,977
nvidia/Nemotron-SFT-Agentic-v2::interactive-agent	51,342	249,330,106	10,992	400	988
nvidia/Nemotron-SWE-v1::r2e-gym	2,658	79,012,723	32,767	0	0
nvidia/Nemotron-SFT-Competitive-Programming-v2::python_00	47,996	165,246,275	16,829	9,788	24,430
nvidia/Nemotron-SFT-Science-v2::rqa_tools	10,326	33,916,631	30,082	1,146	5,501
nvidia/Nemotron-SFT-ARC-AGI-v1::arc_agi_tools	22,027	308,757,443	32,768	12,403	26,978
nvidia/Nemotron-SFT-SWE-v3.5::opencode	271	7,017,212	32,727	457	6,409

Configs

Config	Source	Transform	Splits
agentic_interactive	nvidia/Nemotron-SFT-Agentic-v2	map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
arc_agi_tools	nvidia/Nemotron-SFT-ARC-AGI-v1	map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
chat_multiturn	nvidia/Nemotron-SFT-Instruction-Following-Chat-v3	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
comp_prog_python_00	nvidia/Nemotron-SFT-Competitive-Programming-v2	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
comp_prog_python_01	nvidia/Nemotron-SFT-Competitive-Programming-v2	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
math	nvidia/Nemotron-SFT-Math-v4	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
opencode	nvidia/Nemotron-SFT-OpenCode-v1	map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
science_rqa_plain	nvidia/Nemotron-SFT-Science-v2	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
science_rqa_tools	nvidia/Nemotron-SFT-Science-v2	map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
science_so	nvidia/Nemotron-SFT-Science-v2	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
science_syn_mcq	nvidia/Nemotron-SFT-Science-v2	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
swe_agentless	nvidia/Nemotron-SFT-SWE-v2	map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
swe_opencode_harness	nvidia/Nemotron-SFT-SWE-v3.5	map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
swe_r2e_gym	nvidia/Nemotron-SWE-v1	map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook	none
default	geodesic-research/pa-warm-start-sft-medium-5b-mix	hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → stateful_filter → hook	none

Provenance

agentic_interactive

Source: nvidia/Nemotron-SFT-Agentic-v2 Transform: map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

arc_agi_tools

Source: nvidia/Nemotron-SFT-ARC-AGI-v1 Transform: map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

chat_multiturn

Source: nvidia/Nemotron-SFT-Instruction-Following-Chat-v3 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

comp_prog_python_00

Source: nvidia/Nemotron-SFT-Competitive-Programming-v2 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

comp_prog_python_01

Source: nvidia/Nemotron-SFT-Competitive-Programming-v2 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

math

Source: nvidia/Nemotron-SFT-Math-v4 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

opencode

Source: nvidia/Nemotron-SFT-OpenCode-v1 Transform: map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

science_rqa_plain

Source: nvidia/Nemotron-SFT-Science-v2 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

science_rqa_tools

Source: nvidia/Nemotron-SFT-Science-v2 Transform: map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

science_so

Source: nvidia/Nemotron-SFT-Science-v2 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

science_syn_mcq

Source: nvidia/Nemotron-SFT-Science-v2 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

swe_agentless

Source: nvidia/Nemotron-SFT-SWE-v2 Transform: map_column → map_column → map_column → project → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

swe_opencode_harness

Source: nvidia/Nemotron-SFT-SWE-v3.5 Transform: map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

swe_r2e_gym

Source: nvidia/Nemotron-SWE-v1 Transform: map_column → map_column → map_column → project → filter → filter → map_column → map_column → filter → map_column → map_column → filter → filter → filter → filter → stateful_filter → stateful_filter → hook → hook

python -m dataset_builder configs/medium.yaml --push

default

Source: geodesic-research/pa-warm-start-sft-medium-5b-mix Transform: hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → hf → concat → stateful_filter → hook

python -m dataset_builder configs/medium.yaml --push

Reproducibility

All splits use split_hash() (MD5-based, seeded) so rebuilding from the same config against the same source data produces identical partitions. For an LLM-generated dataset, a provider's seed parameter is best-effort; pin consumer loads to a specific HF commit SHA to avoid drift when the builder re-pushes.

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