Nexus Swarm Worker — FARCA-GRPO Adapter (Qwen3-8B)

LoRA adapter for the worker node in the NEXUS Swarm v4a-max distributed offensive security agent system. Reinforcement-learned from the SFT adapter using FARCA-GRPO (Factuality-Aware Reward with Claim Attribution + Group Relative Policy Optimization).

Role: Worker

The worker is the executor of the swarm. It receives specific, scoped subtasks from the orchestrator and executes them immediately with minimal reasoning. Designed to run on an RTX 3070 Laptop (8 GB) on the control node.

Core behavior:

Execute delegated tasks with correct tool calls — no re-planning
Keep reasoning to one sentence maximum before each tool call
Parse and structure tool output for the orchestrator to consume
Never delegate to other nodes — the worker is the executor

Pipeline Position

DAPT (offsec corpus) → SFT → FARCA-GRPO (this model) → DPO → GGUF

Stage	Model
Base	Qwen/Qwen3-8B
SFT	atlas-institute/qwen3-8b-worker-sft
FARCA-GRPO (this)	atlas-institute/qwen3-8b-worker-farca
GGUF	atlas-institute/qwen3-8b-worker-gguf

Swarm Topology

Node	GPU	Role	Model
nexus-k1	RTX 5060 Ti 16 GB	Orchestrator	Qwen3-14B Q4_K_M
nexus-control	RTX 3070 8 GB	Worker	Qwen3-8B Q4_K_M
nexus-3060	RTX 3060 6 GB	Explore	Gemma-3-4B-it Q8_0
nexus-opi5p	RK3588 NPU	Triage	Qwen3.5-0.8B w8a8
nexus-opi5b	RK3588S ARM	Embeddings	—

Training Details

FARCA-GRPO

FARCA extends standard GRPO with a factuality-aware reward signal. Claims in generated responses are extracted via rule-based methods, verified against reference documents using NLI, and scored for semantic relevance using sentence embeddings.

Prompt dataset: atlas-institute/code-trainer-v11-grpo-worker (400 prompts)
Generations per prompt: 4
Learning rate: 5e-7
KL coefficient: 0.1
Max new tokens: 768
Claim extraction: Rule-based
NLI model: vectara/hallucination_evaluation_model
Sentence encoder: all-MiniLM-L6-v2
NLI weight / Rule weight: 0.4 / 0.6
Precision: BF16
Hardware: NVIDIA A100 80 GB (HuggingFace Jobs)

Worker-Specific Reward Weights

The worker reward function emphasizes execution correctness over reasoning depth:

Component	Weight	Rationale
`has_valid_tool_call_tags`	0.25	Correct format is critical for tool execution
`tool_name_in_schema`	0.20	Must call real tools only
`no_hallucinated_tools`	0.15	No invented tool names
`ends_cleanly_after_tag`	0.15	Clean output termination
`tool_selection_quality`	0.10	Right tool for the job
`reasoning_efficiency`	0.10	Concise reasoning preferred
`has_reasoning_prefix`	0.05	Minimal reasoning — worker shouldn't over-think
`persona_aligned_reasoning`	0.00	Disabled — execution focus, not persona

Validation gate: tool_call_pass_rate ≥ 0.85 across 14 scenarios.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-8B", torch_dtype="auto", device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "atlas-institute/qwen3-8b-worker-farca")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

messages = [
    {"role": "system", "content": "You are a worker node. Execute the delegated task immediately."},
    {"role": "user", "content": "Parse nmap XML output and return open ports with service versions as JSON."},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=768)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Developed By

Atlas Institute — Code-Trainer (RTPI) pipeline.

Framework Versions

PEFT: 0.19.1
Transformers: 5.7.0
TRL: 1.3.0
PyTorch: 2.11.0+cu128

atlas-institute/qwen3-8b-worker-farca