README.md

---
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).