Improve dataset card: Add metadata, links, and sample usage

README.md

@@ -2,6 +2,14 @@
 language:
 - en
 license: apache-2.0
+task_categories:
+- conversational
+- text-generation
+tags:
+- reinforcement-learning-from-human-feedback
+- dialogue
+- conversational-ai
+- preference-alignment
 dataset_info:
 - config_name: feedback_decoder_binary
   features:
@@ -172,4 +180,35 @@ configs:
     path: turn/train-*
 ---
 
-<https://arxiv.org/abs/2410.13852>
+# Retrospective Learning from Interactions (Respect) Dataset
+
+This dataset supports **Retrospective Learning from Interactions (Respect)**, a paradigm introduced in the paper [The Era of Real-World Human Interaction: RL from User Conversations](https://huggingface.co/papers/2509.25137).
+
+The paper introduces Reinforcement Learning from Human Interaction (RLHI), a novel approach that learns directly from in-the-wild user conversations. This enables continual model improvement and multifaceted alignment of conversational models, moving beyond traditional pre-annotated, expert-generated human feedback. The dataset facilitates two complementary methods: RLHI with User-Guided Rewrites and RLHI with User-Based Rewards, linking long-term user personas to turn-level preferences.
+
+*   **Paper**: [The Era of Real-World Human Interaction: RL from User Conversations](https://huggingface.co/papers/2509.25137)
+*   **Project Page**: [https://lil-lab.github.io/respect](https://lil-lab.github.io/respect)
+*   **GitHub Repository**: [https://github.com/lil-lab/respect](https://github.com/lil-lab/respect)
+
+## Sample Usage
+
+You can load the data and associated checkpoints as follows:
+
+```python
+from datasets import load_dataset
+from transformers import Idefics2ForConditionalGeneration
+from peft import PeftModel
+import torch # Ensure torch is imported
+
+# Download data
+ds = load_dataset("lil-lab/respect", name="turn", split="train")
+
+# Download checkpoints
+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")
+```