r0b0tlab/deepseek-v4-pro-0813-agentic

DeepSeek-V4-Pro 0813 Agentic (DS4)

A standalone, verifiable-first agentic training corpus: 19,072 training traces plus 2,135 held-out evaluation rows (validation 1,070 / test 1,065), generated by DeepSeek-V4-Pro 0813 (deepseek-v4-pro-0813, official API, thinking mode) across 13 verifiable task families, each row admitted only after passing a deterministic programmatic verifier. The corpus is designed to be directly usable for SFT, GRPO/RLVR, and NeMo Gym / NeMo RL (verified against the NeMo Gym JSONL contract — see "NeMo-RL readiness" below).

• Teacher: deepseek-v4-pro-0813 (DeepSeek official API, the 2026-08-13 release; visible reasoning captured per turn)
• 19,072 training rows · 1,070 validation · 1,065 test (seed-grouped, zero leakage; 21,207 rows across all splits)
• 13 families · 46.95M input / 16.78M output / 12.15M reasoning tokens
• 100% of rows carry visible reasoning; 27,541 real tool-call steps in 5,997 tool rows; 1,438 error-recovery trajectories; 4,921 rows with parallel tool-call turns
• Every row re-verified by replay (21,207/21,207) and every verifier covered by positive+negative smoke tests (108/108)

Purpose

The corpus fills the domains that benefit agentic performance, following the NVIDIA Nemotron 3 Ultra "teacher" recipe (verifiable instruction-following, structured outputs, programmatic constraint checkers, RLVR reward contracts) and grounded in a full-device analysis of 2,499 real Hermes agent sessions (tool chain shapes, parallel-call rates, recovery shares, reasoning-length bands). See MANIFEST.json for campaign provenance and docs references inside the companion repository.

Families

Family	Rows	What the verifier checks
tool_call_schema_adherence	5,541	native function calls against frozen tool schemas; REAL sandbox execution (fs/sqlite/json tools); final-state equality; recovery + parallel-call lanes; hygiene (thrash detection)
verified_constraint_instruction	4,210	per-row programmatic constraint checkers: keyword count/position, start/end words, paragraph/sentence/word counts, letter bans, punctuation bans, phrase anchors, list items, headers, multi-constraint + negated variants
structured_outputs	1,951	strict parsing (JSON/YAML/XML/TOML/CSV) + schema validation (types/enums/patterns/required, extra-field rejection) + EXACT ground-truth value equality for extraction lanes
stateful_dialogue	1,856	state recall + state mutation across 4 turns (booking/inventory/schedule/budget) with value verifiers
verifiable_math	1,812	exact recomputation (arithmetic/algebra/percentages/fractions/probability/geometry/series) with numeric or Fraction equality
science_verifiable	1,748	independent recomputation (physics kinematics/circuits/KE, chemistry moles, biology Punnett) within tolerance
multilingual_multi_turn	1,649	language script checks (zh/ja/ar/es/fr/de) + bullet-count + 2-sentence constraints across turns
planning_decomposition	761	numbered plan artifact (3-5 steps) + full execution of the plan in the deliverable
grounded_long_context	551	16K-32K synthetic corpora: aggregation, multi-hop, contradiction tasks with required document-ID citations and exact values
delegation_orchestration	397	3-way sub-task decomposition with per-task outputs + merged summary coverage
grounded_citations	361	multi-hop claims over a synthetic corpus with REQUIRED citation formats ([1]/[2] bracket or (doc-0001) inline) and fact checking
memory_context_management	194	summary-based compression and discard-all context management with exact retention semantics
code_execution	176 unique solutions	sandboxed Python with HIDDEN tests (never shown to the model) run on the final code; try-fix trajectories

Splits

Seed-grouped deterministic splits (SHA-256 of family:seed mod 100): train 90% / validation 5% / test 5% — 19,072 / 1,070 / 1,065. Prompt-level grouping guarantees no train/test leakage across seeds.

Repository layout

Four loadable parquet configs (partitioned into train / validation / test shards), plus lossless raw JSONL under raw/ for auditing. Configs are selected by name; raw files are fetched with huggingface_hub (not via load_dataset).

Config	train	validation	test	Contents
canonical	19,072	1,070	1,065	full audit view (all columns below)
sft_openai	19,072	1,070	1,065	SFT-ready view
responses_api	19,072	1,070	1,065	NeMo Gym native view
rl_tool_prompts	15,916	891	895	GRPO prompts + reward contracts

21,207 rows across all splits in canonical/sft_openai/responses_api; rl_tool_prompts covers the 17,702 verifiable rows.

Parquet files are dataset-format sharded (<split>-00000-of-00001.parquet), generated from the raw JSONL via datasets 4.3.0; SHAs are recorded in MANIFEST.json under parquet_configs.

File formats

Config canonical — full audit view (one JSON object per row)

Column	Type	Description
id	string	stable row id, ds4-… prefixed
split	string	train / validation / test
messages	list[dict]	full conversation in OpenAI shape (see below)
tools	list	tool definitions (empty for non-tool families)
task_type	string	ds4-<family>
source	string	ds4-<bucket>
domain	string	family name
subdomain	string	bucket (task variant)
teacher_model / teacher_provider	string	deepseek-v4-pro-0813¹ / deepseek-official-api
trace_kind	string	single_turn (11,967) / multi_turn (3,699) / multi_turn_tool (5,541)
disposition	string	traj_success (19,769) / traj_recovery (1,438 — contains at least one tool error followed by recovery)
ground_truth_json	string	verifier contract / expected values
verifier_passed	bool	always true (acceptance gate)
rubric	list	NVIDIA-style [{question, pass_criteria}]
n_assistant_turns	int	1-12 (median 1, p90 4)
n_tool_calls	int	0-17 (p90 6; 5,997 rows have >=1)
reasoning_present	bool	100% true (visible CoT captured)
seed / attempts	int	deterministic generator seed; attempts until acceptance
schema_version	string	5.0-ds4-production
source_repository / source_license	string	local:supplement_ds4 / other; synthetic research corpus
usage	dict	prompt/completion/reasoning token counts
run_id	string	campaign run provenance

¹ Rows record the API model id as sent during generation (deepseek-v4-pro), which resolved to the 0813 release (deepseek-v4-pro-0813, released 2026-08-13) for every generation run (2026-08-15/16).

Message object shape (variable keys by role; every assistant turn carries reasoning_content when reasoning was produced):

{"role": "system", "content": "..."}
{"role": "user", "content": "..."}
{"role": "assistant", "content": "...", "reasoning_content": "...",
 "tool_calls": [{"id": "call_x", "type": "function",
                 "function": {"name": "fs_read", "arguments": "{\"path\": \"data/a.txt\"}"}}]}
{"role": "tool", "tool_call_id": "call_x", "name": "fs_read", "content": "{\"content\": \"...\"}"}

Config sft_openai — SFT-ready view

{id, split, messages, tools, sampling_weight (1.0), source, domain} — no chat template applied (template at train time, per-model), arguments as JSON STRING per OpenAI format.

Config responses_api — NeMo Gym schema view (trace + verifier contracts)

{id, split, responses_create_params: {input: [messages...]}, verifier_metadata: {task_type, bucket, expected, rubric}, agent_ref: {type: "responses_api_agents", name: "ds4_<family>_simple_agent"}} — matches the NeMo Gym JSONL contract (responses_create_params per the OpenAI Responses API schema; agent_ref routes each row to its agent server; the expected block is the full verifier contract for building the resources-server verify()). NOTE: responses_create_params.input carries the FULL verified reference trajectory (system/user/assistant/tool turns) — use it to build and test verifiers and to replay traces, NOT as a rollout seed. Rollout prompts (prompt-only) live in rl_tool_prompts (verified: zero assistant turns in prompt_messages_json). In the parquet view, verifier_metadata.expected is a JSON STRING (its schema varies by family, which parquet cannot unify); json.loads() it before use. The raw JSONL (raw/responses_api.jsonl) keeps the original nested object.

Config rl_tool_prompts — GRPO prompts + reward contracts (17,702 rows; rollout-ready)

{id, family, bucket, split, prompt_messages_json, tools_json, reward_contract_json, sampling_weight} covering 11 verifiable families — prompt-only messages (verified: zero assistant turns), tool definitions, and reward_contract_json = the exact programmatic contract usable as a reward function. This is the RL rollout view; use it as the NeMo Gym training dataset (prompt + tools + reward contract per row).

Token statistics

• Input 46,952,693 · output 16,779,172 · reasoning 12,150,867
• Token-level blend (share of input+output mass): tool 50.0% · long-context 19.5% · constraint-instruction 9.3% · planning 5.4% · stateful 4.1% · delegation 2.4% · structured-outputs 2.4% · citations 2.2% · multilingual 2.1% · science 0.8% · math 0.7% · memory 0.6% · code 0.5%
• Rows are ordered easy→hard within each family (by attempts-until-acceptance) in canonical.jsonl.

Generation & quality methodology

• Teacher: deepseek-v4-pro-0813 (DeepSeek-V4-Pro, 2026-08-13 release) via the official DeepSeek API; thinking mode; generation parameters: temperature 0.7, top_p 0.95, max_tokens 8,192 (16,384 for long-context).
• Fleet: up to 400 tmux-isolated worker processes over a SQLite-WAL ledger (pending → leased → raw_saved → verified → accepted), crash-safe with orphan-lease recovery, per-family circuit breakers, and a hard budget stop.
• Acceptance requires: structural validity + privacy + dedup + ONE registered objective verifier per family (the table above). Failed rows are retried up to 3 attempts, then retained as rejected evidence — never relabeled.
• Tool rows execute REAL sandbox tools (workspace fs, sqlite, json transforms, python subprocess with timeout); no simulated tool results.
• Every accepted row re-verified by deterministic replay (21,207/21,207).
• Dedup: exact-prompt (max 3 independent completions per unique prompt; tool rows deduped on fixture identity) + 5-gram SimHash (hamming <= 3) on >=300-char non-tool finals; code/planning deduped on prompt+solution identity (only identical solutions collapse).
• Reference bands (from the 2,499-session Hermes tool-use analysis) used by the periodic quality monitor; observed: recovery share 24.0% of tool rows, parallel-turn presence 82.1% of tool rows, reasoning-present 100%.
• PII: synthetic prompts and fixtures only; no mined or user data.

Provenance & licensing

Fully synthetic research corpus. Prompts are deterministic template-generated; every assistant response is a DeepSeek-V4-Pro 0813 (deepseek-v4-pro-0813) output. License: other — synthetic research corpus. Verify DeepSeek's API terms for your intended training use before commercial application. This repo is additive to (not a subset of) r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation: fresh seed namespaces, disjoint templates, and a shingle-blocking cross-dedup gate documented in the manifest.

Usage recipes

SFT (responses-only masking, template at train time):

from datasets import load_dataset
ds = load_dataset("r0b0tlab/deepseek-v4-pro-0813-agentic", "sft_openai")
# messages: list[{role, content, reasoning_content, tool_calls}]
# train assistant turns only; apply your model's chat template at train time.

GRPO/RLVR (NeMo Gym or TRL):

# "responses_api" for NeMo Gym; "rl_tool_prompts" for TRL:
ds = load_dataset("r0b0tlab/deepseek-v4-pro-0813-agentic", "rl_tool_prompts")
# reward_contract_json per row = programmatic verifier contract

Full audit view (all columns):

ds = load_dataset("r0b0tlab/deepseek-v4-pro-0813-agentic", "canonical")

Lossless raw JSONL (byte-identical to generation output, not a datasets config):

from huggingface_hub import hf_hub_download
p = hf_hub_download("r0b0tlab/deepseek-v4-pro-0813-agentic",
                    "raw/canonical.jsonl", repo_type="dataset")

NeMo-RL readiness

Verified 2026-08-16 against the NeMo Gym data-prep contract (docs.nvidia.com/nemo/gym/data) and NVIDIA's released RL datasets (e.g. nvidia/Nemotron-RL-Instruction-Following-Structured-Outputs-v2, nvidia/Nemotron-RL-instruction_following):

Check	Result
responses_create_params present per row (OpenAI Responses API schema)	PASS — 21,207/21,207 rows
agent_ref.type == "responses_api_agents", <family>_simple_agent naming	PASS — matches NVIDIA's own convention
Prompt-only rollout input (NVIDIA ships prompt-only input)	rl_tool_prompts PASS (0 assistant turns) · responses_api carries the full reference trajectory — do not use as rollout seed; project to the pre-assistant prefix (system+user) or use rl_tool_prompts
Programmatic reward contracts	PASS — all 11 RL families carry self-contained contracts (code_exec, math variants, science variants, sqlite/fs tool chains, planning, citations, long-context, compression, delegation, structured-output schema+ground_truth, constraint id+params list mirroring NVIDIA's instruction_id_list/kwargs pattern)
Built-in agent servers	N/A by design — the 13 ds4_<family>_simple_agent names require registering custom NeMo Gym agent servers; verifier_metadata.expected / reward_contract_json provide the verify() contracts, and reference checker implementations ship with the campaign code (scripts/supplement_ds4)
Conversational families (stateful_dialogue, multilingual_multi_turn; 3,505 rows)	NOT in rl_tool_prompts — their RL rollout requires an environment that scripts continuation user turns (judge-style, as NVIDIA's MultiTurnChat)

Consumption path: download the repo → place the raw JSONL views under a resources-server config → run gym dataset collate --mode train_preparation to validate and generate rollout metrics. agent_ref will be matched against your registered agent servers during collation.

Known limitations

• grounded_long_context is 2.6% of rows (16-32K aggregation is hard for the teacher; ~69% yield) — below a 4% inventory floor; supplement for long-context-heavy students.
• memory_context_management yield 48.5% (compression with exact retention).
• code_execution spans 10 task templates; 176 rows, deduped on prompt+solution identity (identical solutions collapse).
• Generation ran during the 2026-08-15/16 DeepSeek pricing change; cost accounting in the campaign reports.

Citation

@misc{r0b0tlab_ds4_0813_2026,
  title = {DeepSeek-V4-Pro 0813 Agentic (DS4): a verifiable-first agentic training corpus},
  author = {r0b0tlab},
  year = {2026},
  url = {https://huggingface.co/datasets/r0b0tlab/deepseek-v4-pro-0813-agentic}
}

Teacher model: DeepSeek-V4-Pro 0813 (deepseek-v4-pro-0813) (DeepSeek, 2026). Methodology informed by the NVIDIA Nemotron 3 Ultra technical report (NVIDIA, 2026).