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base_model:
- HuggingFaceM4/idefics2-8b
language:
- en
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---
# The Era of Real-World Human Interaction: RL from User Conversations
This repository contains the `lil-lab/respect` model, based on the paper [The Era of Real-World Human Interaction: RL from User Conversations](https://huggingface.co/papers/2509.25137).
## Model Description
The model introduces Reinforcement Learning from Human Interaction (RLHI), a paradigm that learns directly from in-the-wild user conversations to achieve continual model improvement and multifaceted alignment. It develops two complementary methods: (1) RLHI with User-Guided Rewrites, which revises unsatisfactory model outputs based on users' natural-language follow-up responses, and (2) RLHI with User-Based Rewards, which learns via a reward model conditioned on knowledge of the user's long-term interaction history (termed persona). These methods link long-term user personas to turn-level preferences via persona-conditioned preference optimization.
## Project Resources
* **Project Page:** [https://lil-lab.github.io/respect](https://lil-lab.github.io/respect)
* **Code Repository:** [https://github.com/lil-lab/respect](https://github.com/lil-lab/respect)
## Sample Usage
To get started with the model, follow these steps:
### 1. Setting up Environment
Prepare your conda environment:
```bash
conda create -n respect python=3.9.18
pip install -r requirements.txt
pip install -e .
```
### 2. Download Data
```python
from datasets import load_dataset
ds = load_dataset("lil-lab/respect", name="turn", split="train")
```
### 3. Load Model Checkpoints
Download checkpoints and load the model using `transformers` and `peft`:
```python
import torch
from transformers import Idefics2ForConditionalGeneration
from peft import PeftModel
checkpoint = "HuggingFaceM4/idefics2-8b"
model_id = 'lil-lab/respect'
model = Idefics2ForConditionalGeneration.from_pretrained(
checkpoint, torch_dtype=torch.bfloat16)
peft_model = PeftModel.from_pretrained(
model, model_id, adapter_name="r6_bp", revision="r6_bp")
```
## Reproducibility
To generate plots from the paper, run `analysis/plots.ipynb` in the [GitHub repository](https://github.com/lil-lab/respect). |