Nemotron-RL-CFBench-v1

Nemotron-RL-CFBench-v1** License: cc-by-4.0 Language: en, ar, hi, zh, ja, ko Task Categories: reinforcement-learning, text-generation Tags: instruction-following, constraint-following, rlvr, nemo-gym Configs: default train split at data/train.jsonl Domain: instruction following, constraint following Modality: text Capability Breakdown: Constraint following [100%] Source: Hybrid: Manually Collected, Synthetic Size Bin: Previous Version(s): N/A License/Terms of Use: This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Intended Usage: This dataset is intended for: Reinforcement learning of LLMs on complex constraint-following prompts. Reinforcement learning with verifiable rewards (RLVR) experiments where rewards measure satisfaction of granular instruction criteria. Training and evaluating robustness to multiple simultaneous user constraints. Studying model behavior on prompts with formatting, keyword, scenario, and other constraint types. Building NeMo Gym-compatible constraint-following environments. Dataset Characterization Dataset Composition and Generation Problem Sources The dataset is manually collected and synthetically augmented. Tasks are instruction-following problems focused on constraint satisfaction. Curation and Filtering RL problems are curated and filtered with GPT-5. Dataset Fields The Ultra-format JSONL file contains the following top-level fields: `agent_ref`: Agent metadata for the VerifIF Gym environment. Records use `responses_api_agents/verifif_simple_agent`. `id`: Numeric example identifier. `instructions`: Structured instruction metadata. Items include fields such as `uid`, `source`, `instruction_id`, `is_misalignment_check`, and task-specific constraint parameters such as keywords. `llm_judge`: Judge checks. Items include `uid`, `source`, `content`, and `is_misalignment_check`. `responses_create_params`: Responses API-style input payload containing system/user messages with optional assistant history. Da