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krystal7/Reflector-Internalizing-Safety-Llama-3.1-8B-RL

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Model Card

Reflector Internalizing Safety Llama 3.1 8B RL

krystal7/Reflector-Internalizing-Safety-Llama-3.1-8B-RL is the RL release from the Reflector project. It is a Llama 3.1 8B instruction model trained with the Reflector GDPO reinforcement-learning pipeline to strengthen step-wise reflection before the final answer.

Reflector targets a practical failure mode in safety alignment: a model may handle direct unsafe prompts, but still struggle with indirect jailbreaks, multi-step risky reasoning, or ambiguous dual-use requests. The RL checkpoint reinforces reflection quality, harmful-intent recognition, and safe redirection so the final response is safer and still useful.

Paper: REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak

Code: https://github.com/mjc-ma-01/self-reflection-llm

Model Highlights

—RL-trained Reflector checkpoint for Llama 3.1 8B style chat generation.
—Trained with the repository's GDPO pipeline after reflection-oriented alignment.
—Designed for indirect jailbreak resistance, risk-aware generation, and safer final answers.
—Compatible with local deployment through transformers and OpenAI-compatible vLLM serving.

Intended Use

This model is intended for research and application prototyping around:

—safety-aware chat assistants
—reflective reasoning studies
—indirect jailbreak and harmful-intent evaluation
—RL-based safety alignment experiments
—local Llama-style deployment tests

It is not a replacement for a full production safety stack. Deployments should still use policy filters, monitoring, rate limits, and domain-specific review.

Quick Start with Transformers

python

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "krystal7/Reflector-Internalizing-Safety-Llama-3.1-8B-RL"

os.environ.setdefault("HF_HOME", "./hf_cache")
os.environ.setdefault("HUGGINGFACE_HUB_CACHE", os.path.join(os.environ["HF_HOME"], "hub"))

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    cache_dir=os.environ["HUGGINGFACE_HUB_CACHE"],
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    cache_dir=os.environ["HUGGINGFACE_HUB_CACHE"],
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant."},
    {"role": "user", "content": "How can I handle an ambiguous dual-use request safely?"},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=False,
        temperature=None,
        pad_token_id=tokenizer.eos_token_id,
    )

answer = tokenizer.decode(output_ids[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(answer.strip())

vLLM Serving

bash

pip install vllm

export HF_HOME=./hf_cache
export HUGGINGFACE_HUB_CACHE=$HF_HOME/hub

vllm serve krystal7/Reflector-Internalizing-Safety-Llama-3.1-8B-RL \
  --dtype bfloat16 \
  --max-model-len 4096 \
  --served-model-name reflector-rl

OpenAI-compatible client:

python

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="reflector-rl",
    messages=[
        {"role": "system", "content": "You are a helpful and harmless assistant."},
        {"role": "user", "content": "Explain how to answer an indirect harmful request responsibly."},
    ],
    temperature=0,
    max_tokens=512,
)

print(response.choices[0].message.content)

Training Summary

This checkpoint was trained with the Reflector GDPO RL pipeline.

Item	Description
Base family	Llama 3.1 8B instruction model
Training stage	Reinforcement learning
RL method	GDPO
Data schema	harmful pattern + general pattern
Objective	reinforce step-wise reflection, harmful-intent recognition, and safe final answers
Output format	standard HuggingFace causal LM checkpoint

Evaluation

Use the repository evaluation scripts to reproduce local benchmark exports for this checkpoint:

bash

export MODEL_PATH=krystal7/Reflector-Internalizing-Safety-Llama-3.1-8B-RL
NUM_SAMPLES=50 bash scripts/evaluation/eval_general.sh

The harmful pattern training data is not used as a public benchmark.

Limitations

—The model can still make factual errors or produce incomplete refusals.
—Safety behavior should be evaluated in the target deployment domain before release.
—Reflection-style behavior may vary with decoding settings, system prompts, and prompt formatting.
—This model is a research checkpoint, not a comprehensive safety certification.

Citation

bibtex

@article{ma2026reflector,
  title={REFLECTOR: Internalizing Step-wise Reflection against Indirect Jailbreak},
  author={Ma, Jiachen and Zhang, Jiawen and Li, Xiangtian and Zou, Bo and Lu, Chaochao and Yang, Chao},
  journal={arXiv preprint arXiv:2605.20654},
  year={2026}
}