| --- |
| license: apache-2.0 |
| language: |
| - en |
| pretty_name: StepGuard-Data |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - agent-safety |
| - tool-use |
| - step-level-safety |
| - supervised-fine-tuning |
| - reinforcement-learning |
| configs: |
| - config_name: sft3k |
| data_files: |
| - split: train |
| path: sft3k/agentguard_sft3k_sharegpt.jsonl |
| - config_name: rl4k |
| data_files: |
| - split: train |
| path: rl4k/agentguard_rl4k_grpo.jsonl |
| --- |
| |
| # StepGuard-Data |
|
|
| **7K training examples for agent safety · 3K SFT + 4K RL · EMNLP 2026** |
|
|
| [Paper](https://arxiv.org/abs/2608.24777) · [Model](https://huggingface.co/ninty-seven/StepGuard) · [Code](https://github.com/zheng977/StepGuard) · [Project](https://zheng977.github.io/StepGuard/) |
|
|
| This dataset releases the final **7,000-example training corpus** used for StepGuard, a 4B guard that checks proposed tool actions before execution and audits completed agent trajectories. StepGuard is fine-tuned from **Qwen3-4B-Instruct-2507** using **StepGen** supervision and **Balance-GRPO**. |
|
|
| The corpus contains two training stages with separate schemas: **SFT-3K** provides prompts paired with structured safety responses; **RL-4K** provides prompts and safety labels for reinforcement learning. Both cover action-level guarding and trajectory-level auditing. |
|
|
| ## Dataset at a glance |
|
|
| | Subset | Training stage | Examples | Action-level | Trajectory-level | Safe | Unsafe | |
| | --- | --- | ---: | ---: | ---: | ---: | ---: | |
| | **SFT-3K** | Supervised fine-tuning | 3,000 | 1,500 | 1,500 | 1,500 | 1,500 | |
| | **RL-4K** | Balance-GRPO | 4,000 | 2,000 | 2,000 | 2,000 | 2,000 | |
| | **Total** | SFT → RL | **7,000** | **3,500** | **3,500** | **3,500** | **3,500** | |
|
|
| A safe decision can still occur in the presence of a risk source. SFT-3K includes 300 benign (`risk_source=none`) examples and 1,200 safe examples with a non-`none` risk source; RL-4K includes 600 and 1,400, respectively. Safety judgment and risk-source attribution are separate targets. |
|
|
| These are training subsets, each exposed as a `train` split. For the evaluation benchmarks and results, see the [paper](https://arxiv.org/abs/2608.24777) and [model card](https://huggingface.co/ninty-seven/StepGuard). |
|
|
| ## Files |
|
|
| | File | Contents | |
| | --- | --- | |
| | [`sft3k/agentguard_sft3k_sharegpt.jsonl`](sft3k/agentguard_sft3k_sharegpt.jsonl) | 3,000 ShareGPT-format SFT examples | |
| | [`rl4k/agentguard_rl4k_grpo.jsonl`](rl4k/agentguard_rl4k_grpo.jsonl) | 4,000 RL prompts with safety labels | |
| | [`manifests/sft3k_manifest.json`](manifests/sft3k_manifest.json) | SFT sampling targets and composition statistics | |
| | [`manifests/rl4k_manifest.json`](manifests/rl4k_manifest.json) | RL sampling targets, source-pool counts, and quality-check summaries | |
|
|
| The two JSONL files are kept separate because they serve different training stages and use different schemas. Manifests describe the sampling process; they are not training examples. |
|
|
| ## Quick start |
|
|
| The repository is public and does not require an access request. |
|
|
| ```bash |
| pip install datasets |
| ``` |
|
|
| Load the two subsets independently: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| repo_id = "ninty-seven/stepguard_data" |
| sft = load_dataset(repo_id, "sft3k", split="train") |
| rl = load_dataset(repo_id, "rl4k", split="train") |
| |
| assert len(sft) == 3000 |
| assert len(rl) == 4000 |
| |
| sft_prompt = sft[0]["conversations"][0]["value"] |
| sft_response = sft[0]["conversations"][1]["value"] |
| rl_prompt = rl[0]["instruction"] |
| rl_label = rl[0]["label"] |
| ``` |
|
|
| Use the released [training recipe](https://github.com/zheng977/StepGuard/tree/main/training/release) for SFT and Balance-GRPO. The [prompt templates](https://github.com/zheng977/StepGuard/blob/main/src/guardrail/prompts/stepguard.py) and [input-format guide](https://github.com/zheng977/StepGuard/blob/main/docs-open/prompts.md) describe how requests, tools, actions, and observations are serialized. |
|
|
| ## Data formats |
|
|
| ### SFT-3K |
|
|
| Each row contains one `conversations` array with a `human` prompt followed by a `gpt` reference response. The following is a schema illustration; placeholders are not an actual training example. |
|
|
| ```json |
| { |
| "conversations": [ |
| {"from": "human", "value": "<guard prompt and agent context>"}, |
| {"from": "gpt", "value": "<structured safety response>"} |
| ] |
| } |
| ``` |
|
|
| Reference responses contain: |
|
|
| ```text |
| <Analysis>Safety rationale</Analysis> |
| <Judgment>safe or unsafe</Judgment> |
| <RiskSourcePresent>yes or no</RiskSourcePresent> |
| <RiskSource>risk-source label</RiskSource> |
| ``` |
|
|
| Trajectory-level responses additionally contain `<UnsafeStep>None or an integer step ID</UnsafeStep>`. Step IDs refer to agent actions. See the model card for the full output contract and inference prompts. |
|
|
| ### RL-4K |
|
|
| | Field | Type | Meaning | |
| | --- | --- | --- | |
| | `id` | string | Unique sample identifier: `stepguard-rl-000001` through `stepguard-rl-004000` | |
| | `instruction` | string | Guard prompt with the action or trajectory context | |
| | `output` | string | Reference safety label: `safe` or `unsafe` | |
| | `label` | string | Safety label; identical to `output` in this release | |
| | `level` | string | `action` or `trajectory` | |
| | `risk_source` | string | Risk-source category | |
|
|
| **RL `output` is a class label, not a full rationale or a structured SFT response.** Use this file with the RL training recipe rather than treating its labels as the SFT response format. The prompt is stored in `instruction`; safety labels are stored in `output` and `label`. |
|
|
| ## Risk-source categories |
|
|
| Both subsets use the following nine categories, including `none` for benign context: |
|
|
| ```text |
| none |
| malicious_user_instruction_or_jailbreak |
| direct_prompt_injection |
| indirect_prompt_injection |
| unreliable_or_misinformation |
| tool_description_injection |
| malicious_tool_execution |
| corrupted_tool_feedback |
| inherent_agent_failure |
| ``` |
|
|
| Per-category counts and sampling details are provided in the manifests. Category counts are not uniform, even though each subset has equal numbers of safe and unsafe examples. |
|
|
| ## License |
|
|
| The StepGuard-Data release is licensed under the [Apache License 2.0](LICENSE) for the rights held by the StepGuard contributors in the training examples, annotations, compilation, and accompanying documentation. Commercial use, training, modification, and redistribution are permitted under its terms; we impose no research-only or non-commercial restriction on these contributions. |
|
|
| The corpus was constructed using StepGen, as described in the [paper](https://arxiv.org/abs/2608.24777). Referenced third-party tools, services, and any incorporated third-party material remain subject to their respective rights and applicable terms; this license does not relicense third-party material or grant access to external services. Evaluation benchmarks linked from the code repository are separate releases and retain their own licenses. |
|
|
| ## Citation |
|
|
| If you use this dataset or StepGuard, please cite our paper: |
|
|
| ```bibtex |
| @misc{zheng2026stepguardlearningsteplevelguardrails, |
| title = {StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety-Utility Balancing}, |
| author = {Zhijie Zheng and Yu Li and Chen Qian and Yuqian Fu and Yanwei Fu and Lu Sheng and Jing Shao and Dongrui Liu}, |
| year = {2026}, |
| eprint = {2608.24777}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.AI}, |
| url = {https://arxiv.org/abs/2608.24777} |
| } |
| ``` |
|
|
