README.md

---
license: cc-by-nc-4.0
language:
  - en
task_categories:
  - text-generation
tags:
  - safety
  - jailbreak-defense
  - multi-turn
  - reinforcement-learning
  - trace
size_categories:
  - 1K<n<10K
configs:
  - config_name: curated
    data_files:
      - split: train
        path: data/curated_train.parquet
      - split: validation
        path: data/curated_val.parquet
  - config_name: harm_adjacent
    data_files:
      - split: train
        path: data/harm_adjacent_train.parquet
      - split: validation
        path: data/harm_adjacent_val.parquet
extra_gated_heading: Request access to the TRACE RL split
extra_gated_description: >-
  This split contains multi-turn adversarial conversations that pursue harmful behaviors from
  HarmBench and JailbreakBench. The assistant turns are refusals or bounded responses and no row
  is a working recipe for harm, but the attacker turns are real jailbreak trajectories. Access is
  granted for safety research: training and evaluating defenses, and reproducing the results in
  the TRACE paper.
extra_gated_prompt: >-
  By requesting access you confirm that you will use this data for safety research only; that you
  will not use it to develop, train or improve jailbreak attacks, nor to elicit harmful content
  from any model in deployment; that you will not redistribute the data or derivatives of it
  outside your research group without equivalent access terms; that you accept the original terms
  of HarmBench and JailbreakBench, which cover the underlying behaviors; and that you will cite
  the TRACE paper in work that uses it.
extra_gated_fields:
  Full name: text
  Affiliation: text
  Country: country
  Role:
    type: select
    options:
      - Academic researcher
      - Industry researcher
      - Student
      - Independent researcher
      - Other
  How you intend to use the data: text
  I will use this data for safety research only: checkbox
  I will not use it to develop or improve jailbreak attacks: checkbox
  I will not redistribute it outside my research group: checkbox
  I accept the HarmBench and JailbreakBench terms for the underlying behaviors: checkbox
extra_gated_button_content: Request access
---

# TRACE — RL split

The reinforcement-learning data for [TRACE](https://github.com/Dipto084/TRACE), the trajectory-aware
multi-turn jailbreak defense ([arXiv:2608.15594](https://arxiv.org/abs/2608.15594)). These are the
exact files the GDPO stage consumed, in the [verl](https://github.com/volcengine/verl) RL data
format:

```
data.train_files=[curated_train.parquet, harm_adjacent_train.parquet]
data.val_files=[curated_val.parquet,     harm_adjacent_val.parquet]
```

Each row is one **trajectory** — a conversation up to and including the user turn the policy must
answer — paired with a reference safety assessment used to compute the reward.

## Configs

| Config | Split | Rows | What it is |
|---|---|---|---|
| `curated` | train | 5,234 | multi-turn adversarial + benign trajectories |
| `curated` | validation | 1,240 | |
| `harm_adjacent` | train | 500 | sensitive-but-benign trajectories (over-refusal pressure) |
| `harm_adjacent` | validation | 102 | |

The two are mixed per batch by a stratified sampler (56 curated / 8 harm-adjacent at batch size 64),
so the safety and over-refusal objectives enter every gradient step rather than being balanced
afterwards.

### Composition of `curated`

| | train | validation |
|---|---|---|
| Attack frameworks | crescendo 1,252 · xteaming 1,188 · actor 1,077 · coa 598 · icon 204 | 289 · 299 · 258 · 122 · 45 |
| Benign (no attack type) | 915 | 227 |
| Behavior source | harmbench 2,951 · jailbreakbench 1,368 | 647 · 366 |
| Benign source | ultrachat 368 · sharegpt 372 · wildchat 175 | 101 · 81 · 45 |
| Jailbreak score 1–5 | 1,297 / 1,299 / 679 / 1,500 / 459 | 325 / 325 / 121 / 375 / 94 |

`harm_adjacent` is generated from OR-Bench-style seeds (`claude_harm_adjacent`) and skews benign:
scores 1–2 cover 453 of 500 training rows.

## Schema

| Field | Type | Notes |
|---|---|---|
| `data_source` | string | `jailbreak_curated` or `harm_adjacent`; the sampler strata |
| `prompt` | list of `{role, content}` | system prompt + the trajectory as a single user message in `[Turn N]` form |
| `ability` | string | `safety` |
| `reward_model.ground_truth` | struct | reference `cues` (12 manipulation-cue fields, each a justification string), `jailbreak_score` (1–5), `last_user_turn`, `gt_answer_length` |
| `reward_model.style` | string | reward style tag |
| `extra_info` | struct | `attack_type`, `behavior_id`, `dataset`, `index`, `jailbreak_score` |

| `conversation` | list of `{turn, role, content}` | the same trajectory parsed into turns, so no one has to re-parse the `[Turn N]` text |
| `reference_response` | string | the annotated target for this trajectory: `<STATE>{...}</STATE><ANSWER>...</ANSWER>` |

The reward components (`R_jb` jailbreak-score accuracy, `R_cue` cue-set agreement, `R_con`
behavioral consistency) are computed against `reward_model.ground_truth`; see the paper and the
reward implementation for the exact formulation. Note that the RL reward used
`gt_answer_length`, not the reference text -- `reference_response` is included here so the same
rows can be used for supervised or distillation setups, and so the labels can be audited against
the annotation they came from.

The STATE and ANSWER blocks are not stored separately, since both parse out of
`reference_response`:

```python
import json, re
state = json.loads(re.search(r"<STATE>(.*?)</STATE>", ref, re.S).group(1))
answer = re.search(r"<ANSWER>(.*?)</ANSWER>", ref, re.S).group(1).strip()
```

## Usage

```python
from datasets import load_dataset

curated = load_dataset("Dipto084/TRACE_RL_Dataset", "curated")
harm_adj = load_dataset("Dipto084/TRACE_RL_Dataset", "harm_adjacent")
print(curated["train"][0]["extra_info"])
```

For RL training, point verl at the parquet files directly rather than loading through `datasets` —
that is how the reported runs were configured.

These rows were produced from the annotated SFT records by extracting `cues` and `jailbreak_score` from each target's STATE block, with score-weighted sampling that upweights the hardest bands (scores 3-4).

## Provenance

Behaviors come from [HarmBench](https://www.harmbench.org/) and
[JailbreakBench](https://jailbreakbench.github.io/); adversarial conversations were produced by
running five attack frameworks (Crescendo, ActorAttack, Chain-of-Attacks, ICON, X-Teaming) against
target models, and benign conversations are drawn from public multi-turn corpora. Every trajectory
was annotated by a frontier LLM with the full reasoning trace (cues, dual-hypothesis scores,
jailbreak score, action).

## Intended use and caution

This is **defense-training data**, released so the TRACE results can be reproduced. It contains
multi-turn adversarial conversations targeting harmful behaviors; the assistant turns are refusals
or bounded responses, and no row is a working recipe for harm. Use it for safety research —
training and evaluating defenses — not for building attacks. The behaviors it references are
already public in HarmBench and JailbreakBench.

## Citation

```bibtex
@article{miah2026trace,
  title   = {TRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation},
  author  = {Miah, Md Messal Monem and Anika, Adrita and Yu, Zhiyuan and Huang, Ruihong},
  journal = {arXiv preprint arXiv:2608.15594},
  year    = {2026}
}
```