opensec-gdpo-4b

OpenSec GDPO 4B (Phase 2)

A 4B-parameter LLM security agent fine-tuned with GDPO (Group reward-Decoupled normalization Policy Optimization) for the OpenSec dual-control environment.

Model Details

• Base model: Qwen3-4B-Instruct-2507
• Training framework: Slime (Megatron + SGLang async on-policy RL)
• Algorithm: GRPO with GDPO per-axis reward normalization
• Training epochs: 8 (best checkpoint at epoch 4)
• Hardware: 2x NVIDIA H100 PCIe (80GB)
• Attacker: GPT-5.2 replay cache (deterministic, 2,263+ cached decisions)

Training Configuration

Evaluation Results (ep4 checkpoint, 40 standard eval seeds)

Reward Axes

Training uses 5 reward axes with per-axis GDPO normalization:

• Attribution: Correct entity identification in reports
• Containment: +1.0 per correct action, -1.0 per false positive (uncapped)
• Gating: -1.5 per containment action without prior trusted evidence
• Efficiency: -0.1 per step (scaled by epoch)
• Report: +3.0 for correct submission, -3.0 for malformed/missing

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Jarrodbarnes/opensec-gdpo-4b",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Jarrodbarnes/opensec-gdpo-4b")

For evaluation within the OpenSec environment:

python scripts/eval.py --model Jarrodbarnes/opensec-gdpo-4b --seeds standard-40

Limitations

• EGAR improvement is modest (+0.013) and not statistically significant (95% CI: [-0.067, +0.100])
• FP rate increased (+0.075), indicating the model learned to always execute containment rather than improving discrimination
• Training with GDPO per-axis normalization showed signal starvation at low temperatures; future work should evaluate standard GRPO normalization

Citation

@misc{opensec2026,
  title={OpenSec: A Dual-Control RL Environment for Evaluating LLM Security Agents},
  author={Barnes, Jarrod},
  year={2026},
  url={https://github.com/jarrodbarnes/opensec-env}
}