hungbenjamin402/Nemotron-RL-IF-Gym-LFM2.5-prompts

Nemotron-RL instruction-following & abstention Gym tasks → LFM2.5 prompt format

Verifiers are NVIDIA NeMo-Gym rule checkers, not IFEval. Each row keeps the full verifier metadata in extra (verifier regex/string-match specs, schema_str/schema_type, exp_cal_state, answer) so the checks can be re-implemented or run through NeMo Gym's resources servers. qa_abstention rows carry a per-row license of CC BY-SA 4.0 from the source.

A derivative of [five nvidia/Nemotron-RL-* NeMo-Gym datasets](https://huggingface.co/datasets/five nvidia/Nemotron-RL-* NeMo-Gym datasets) (cc-by-4.0) normalized for fine-tuning Liquid AI LFM2 / LFM2.5 models, whose native tool-call format is Pythonic:

<|im_start|>assistant
<|tool_call_start|>[get_weather(location='Paris, France', unit='celsius')]<|tool_call_end|><|im_end|>

Prompt-only rows (prompt_only = true) from five rule-verified NVIDIA RL datasets, one split each: structured_outputs_direct (JSON/XML/YAML/TOML schema-conformant generation), structured_outputs_diversified (document → schema extraction, multi-step), structured_outputs_tool_extraction (extraction through a tool call — tools attached, answer must be a Pythonic call to the right response_tool_N among distractors), free_form_formatting (regex-verified layouts), citation_formatting ([ref:N] markers), calendar (multi-turn scheduling with expected calendar state), qa_abstention (answer or abstain; target answer in extra). Rows whose prompt exceeds 8,192 tokens are dropped (the serving context for our LFM2.5 deployment is 10,240): this removes roughly half of structured_outputs_tool_extraction and a fifth of structured_outputs_diversified (long source documents plus many distractor tools).

Every row was rendered through the official LiquidAI/LFM2.5-VL-3B chat template (identical to the LFM2.5 text models' template for text-only input); prompt-only rows carry no tool calls, so no round-trip check applies.

What changed vs. the source

Transform	Why
responses_create_params.input → messages, .tools (flat Responses-API tools, parameters JSON string) → OpenAI tool schemas; rendered with add_generation_prompt=True	LFM renders tools as List of tools: [...]; policy answers with a Pythonic call.
All other top-level fields → extra (verifier specs, schemas, expected states, answers)	Nothing about the reward is lost.
Tool names sanitized to Python identifiers (web-search → web_search), consistently in tools and calls; originals kept in renamed_tools	Pythonic call syntax requires identifiers.
Exact-duplicate tool entries deduplicated	Template hygiene.
Dropped: calls to undeclared tools, unparseable arguments, argument names that are not identifiers or are Python keywords (from, class), rows over 8192 tokens, rows with no assistant tokens	Training hygiene.

Nothing was rephrased, re-generated, or re-labelled.

Columns

Column	Type	Description
id, source, split, license	str	provenance
messages	str (JSON)	canonical OpenAI-style messages; tool_calls arguments are dicts. Feed this + tools to apply_chat_template to re-render with any LFM template version.
tools	str (JSON)	OpenAI-style tool schemas
text	str	fully rendered LFM2.5 conversation, BOS included
prompt_only	bool	true when the row is a prompt ending in the generation prompt (no assistant tokens)
n_turns, n_tool_calls, n_tools, n_tokens, n_assistant_tokens	int	sizes (LFM2.5 tokenizer)
renamed_tools	str (JSON)	{original: sanitized} when any tool was renamed, else ""
extra	str (JSON)	source-specific fields: verifier, schema_str, schema_type, problem_type, exp_cal_state, question, answer, domain, agent, tool_choice, parallel_tool_calls (per split)

Stats

Split	Read	Kept	Rows with renamed tools	Dropped (reason=count)
structured_outputs_direct	28,145	28,145	0
structured_outputs_diversified	25,768	22,581	0	too_long=3,187
structured_outputs_tool_extraction	8,783	4,534	0	too_long=4,249
free_form_formatting	9,037	9,036	0	too_long=1
citation_formatting	9,540	9,540	0
calendar	9,659	9,628	0	too_long=31
qa_abstention	3,150	3,150	0

Training notes

• Use assistant-only loss: apply_chat_template(messages, tools=tools, tokenize=True, return_assistant_tokens_mask=True).
• Prefer re-rendering from messages/tools over training on text if your template differs.
• Serving-side parsers must accept JSON literals (true, null, nested {}/[]) inside Pythonic calls.

Citation

Please cite the upstream dataset: https://huggingface.co/datasets/five nvidia/Nemotron-RL-* NeMo-Gym datasets