Retrieval-augmented generation
RAG-DDR
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
Superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 3 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites RAG-DDR as a baseline.
However, their success require the model to construct correct reasoning paths.
Beaten on benchmarks
Head-to-head results where a newer method reports beating RAG-DDR. Values are copied from the source paper's tables — verify against the cited paper.
ClueAnchor beats RAG-DDR
24.67 vs 20.79
MusQ · [Llama-3.1-Instruct 8B]
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented GenerationMMOA-RAG beats RAG-DDR
38.85 vs 36.25
AffectAgent beats RAG-DDR
76.78 vs 75.42
Mean · [full-modality (audio, video, text)]
AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.