PolicyTraj-20K

PolicyTraj-20K is the training and evaluation data for RePolicy-4B, an agent safeguard that learns safety-policy invocation through reinforcement learning.

Each example pairs an agent trajectory with a local safety-policy library and annotates which policy governs the judgment, which clauses are violated, the safety label, and a decision rationale. This gives both trajectory-level supervision and clause-level policy grounding in one resource.

• Code: https://github.com/JiangHoucheng/RePolicy
• Model: https://huggingface.co/JiangHoucheng/RePolicy-4B

Splits

Config	Split	Examples	Format	Purpose
sft	train	5,000	parquet	Cold-start SFT
sft	validation	500	parquet	SFT validation
rl	train	14,425	parquet	GRPO training
rl	validation	500	parquet	GRPO validation
eval	test	7,369	parquet	Six-benchmark evaluation

The SFT and RL splits total 20,425 policy-grounded trajectories and are disjoint by construction. The RL train split is 8,186 unsafe / 6,239 safe.

policies/policy_library.json holds the 30-policy, 358-clause library the Policy_N ids in every example refer to.

All splits are parquet (zstd). extra_info.tools_kwargs is a nested struct, not a string, because verl indexes into it during rollout; the local_to_global_* / global_to_local_* id maps are JSON strings, since their keys differ in every example and would otherwise force a distinct arrow schema per row.

Schema

rl and eval (verl-compatible parquet)

{
  "data_source": "agent_guard",              # or agent_guard_benchmark/<name>
  "agent_name": "tool_agent",
  "prompt": [                                 # policy TITLES only, no clause text
    {"role": "system", "content": "You are an agent safety guard. ..."},
    {"role": "user", "content": "<AGENT_TRAJECTORY>...</AGENT_TRAJECTORY>\n\n<POLICY_LIBRARY>...</POLICY_LIBRARY>"}
  ],
  "ability": "agent_safety_guard",
  "reward_model": {"style": "rule", "ground_truth": {"label": "unsafe", "rationale": "...", "answer": "..."}},
  "extra_info": {
    "gold_label": "unsafe",
    "gold_local_policy_ids": ["Policy_24"],           # policy that should be invoked
    "gold_local_violated_clause_ids": ["Policy_24.2"],
    "decoy_local_policy_ids": ["Policy_9", "Policy_13"],
    "local_to_global_policy": "{...}",                 # JSON string, id remapping
    "tools_kwargs": {"get_policy": {"create_kwargs": {"policy_library": [...]}}}
  }
}

The prompt deliberately exposes only policy ids and titles. Full clause text lives in extra_info.tools_kwargs, served to the model when it calls get_policy — so a model must decide which policy to invoke before it can read any requirements.

sft (parquet)

messages, tools, enable_thinking, extra_info. Messages follow the five-turn supervision path with per-message loss masks:

#	Role	Loss	Content
1	system	✗	Guard instruction
2	user	✗	Trajectory + policy titles
3	assistant	✓	get_policy tool call
4	tool	✗	Retrieved clauses
5	assistant	✓	Rationale + <JUDGE> tag

Policy-context perturbation

Local libraries are built by mixing the gold policy with distractors, then shuffling. Distractors are real policies from unrelated operation scenes plus synthetic decoys (e.g. "Calendar Color Theme") that are plainly non-safety. In the RL train split each library holds 15-35 policies (mean 28.9) with a mean of 9.2 decoys and 1.64 gold policies. Because ids are re-indexed per example, the same underlying policy appears under different Policy_N ids across examples, which prevents memorising fixed trajectory-policy associations.

The eval split instead uses the full 30-policy real library with no decoys, so evaluation is deterministic and comparable across models.

Usage

from datasets import load_dataset

sft = load_dataset("JiangHoucheng/PolicyTraj-20K", "sft")
rl = load_dataset("JiangHoucheng/PolicyTraj-20K", "rl")
bench = load_dataset("JiangHoucheng/PolicyTraj-20K", "eval", split="test")

To train with it, clone the code repo and use the provided scripts — they expect this exact data/{sft,rl,eval} layout:

bash repolicy/scripts/download_data.sh
bash repolicy/scripts/run_sft.sh
bash repolicy/scripts/run_grpo.sh

Construction

Agent interaction patterns were collected from existing agent safety resources and normalized into a common trajectory representation. An API-based model then resampled task content — user requests, entities, files, URLs, arguments, and observations — while preserving trajectory structure, tool-use pattern, and the underlying safety mechanism. Samples with inconsistent tool interactions, unsupported labels, or heavy overlap with their reference were removed.

Safety policies are organized by agent operation scene rather than final risk category, so one policy captures the requirements for a class of agent operations. The library was audited against all trajectories: requirements were refined where coverage was thin, new policies added only for genuinely distinct scenes, and overlapping policies merged. Coverage of the evaluation split is 100% with a mean of 1.62 applicable policies per trajectory.

Limitations

• Trajectories are largely synthetic expansions of existing safety resources; surface diversity exceeds that of the seed pool, but the underlying safety mechanisms derive from it.
• Each trajectory is grounded to a single governing policy, while real deployments may need several policies applied jointly.
• English only, binary safe/unsafe labels, offline trajectories that are not re-executed.
• Rationales are model-generated and audited for policy grounding, not verified sentence by sentence.
• Trajectories describe unsafe agent behavior for safeguard research; the content is intended for training and evaluating guards, not for reuse as instructions.

License

Apache 2.0. Derived-from benchmarks retain their original licenses; cite them alongside this dataset.