code-trainer-v10-dpo-pairs

Preference pair dataset for Direct Preference Optimization (DPO) training, built from real offensive security agent sessions and synthetic degradations. Used by both the Qwen and Gemma Code-Trainer pipelines for the DPO RL stage.

Part of the Code-Trainer / RTPI pipeline (GitHub).

Dataset summary

Split	Pairs
Train	783
Validation	87
Total	870

Format

Each row is a preference triple:

{
  "prompt": "...",
  "chosen": "...",
  "rejected": "..."
}

• prompt — the user message (offensive security task, tool-use scenario, or multi-step agent instruction)
• chosen — the preferred response (from a real OCO session trace)
• rejected — a degraded response (synthetically generated)

Data sources

Positives (chosen responses)

Extracted from 221 OCO (Offensive Cyber Operations) sessions across three platforms:

Source	Description
HackTheBox	Completed room histories with tool-call traces
TryHackMe	Completed room histories with tool-call traces
Bug bounty	Real-world bug bounty agent session traces

Sessions are synced from edge Kali containers via Cloudflare R2 (scripts/pull_sessions_from_r2.sh) and ingested via src/phase4c_rl/data/ingest_oco_sessions.py.

Negatives (rejected responses)

Synthetically generated from the positive responses via 4 degradation strategies:

Strategy	Description
Refusal	Replaces the response with a safety-refusal message
Stripped tool calls	Removes all <tool_call> tags, leaving only prose
Truncated	Cuts the response mid-completion
Hallucinated commands	Replaces tool arguments with plausible but incorrect commands

Built by src/phase4c_rl/data/collect_negatives.py --synthetic.

Persona pairs (V4.0)

~400 additional pairs targeting persona alignment:

• Chosen: identity-aligned responses from Nexus identity training examples (identifies as Nexus, offsec framing, MITRE ATT&CK, scope-first)
• Rejected: vanilla AI assistant responses ("I'm a large language model trained by Google", "helpful, harmless, honest", blanket refusals)

Built by src/phase4c_rl/data/build_dpo_pairs.py --identity-examples data/identity_examples/nexus_identity.jsonl.

Build pipeline

# 1. Pull sessions from R2
bash scripts/pull_sessions_from_r2.sh

# 2. Ingest OCO sessions
python -m src.phase4c_rl.data.ingest_oco_sessions \
    --input-dir data/cot_rl_sessions \
    --output-dir data/oco_converted \
    --format json

# 3. Generate synthetic negatives
python -m src.phase4c_rl.data.collect_negatives --synthetic

# 4. Build preference pairs
python -m src.phase4c_rl.data.build_dpo_pairs

Intended use

• DPO training: used by gemma4-26b-a4b-code-trainer-v10-dpo and the corresponding Qwen DPO adapter to align model outputs with human-demonstrated offensive security workflows.
• Out of scope: this dataset contains offensive security tool-call traces from controlled environments (HTB, THM, bug bounty programs). It is not intended for training models for unauthorized access.

Limitations

• Small dataset. 870 pairs is at the lower end for DPO — sufficient for a fine-tuning signal on top of a strong SFT foundation, but gains may saturate quickly.
• Synthetic negatives. The rejected responses are algorithmically degraded, not human-judged. This means the preference signal captures format/completeness rather than nuanced quality differences.
• Domain-specific. All positives come from offensive security contexts; the preference signal may not generalize to other coding domains.

Reproducibility

• Code: github.com/cmndcntrlcyber/code-trainer-pipeline (src/phase4c_rl/data/)