Unified Retrieval and Generation Cuts RAG Complexity
Researchers propose GRIP, a framework that integrates retrieval decisions directly into the token generation process rather than treating retrieval as a separate external step. The model learns to emit control tokens that signal when to retrieve information, how to reformulate queries, and when to stop, all within a single autoregressive pass. Trained on structured datasets covering answerable, partially answerable, and multi-hop questions, GRIP matches or exceeds strong RAG baselines and approaches GPT-4o performance while using substantially fewer parameters.
Researchers propose GRIP, a framework that integrates retrieval decisions directly into the token generation process rather than treating retrieval as a separate external step. The model learns to emit control tokens that signal when to retrieve information, how to reformulate queries, and when to stop, all within a single autoregressive pass. Trained on structured datasets covering answerable, partially answerable, and multi-hop questions, GRIP matches or exceeds strong RAG baselines and approaches GPT-4o performance while using substantially fewer parameters.
- GRIP embeds retrieval control into token-level decoding, eliminating the need for separate retrieval controllers or classifiers
- Self-Triggered Information Planning allows the model to autonomously decide when to retrieve, reformulate queries, and terminate retrieval within a single generation trajectory
- Training uses structured datasets aligned with specific token patterns for answerable, partially answerable, and multi-hop queries
- Evaluation on five QA benchmarks shows GRIP outperforms existing RAG baselines and is competitive with GPT-4o at lower parameter counts
This work addresses a fundamental architectural inefficiency in current RAG systems, where retrieval and generation operate as separate components requiring external coordination. By unifying retrieval and generation into a single token-level process, GRIP reduces latency, improves end-to-end reasoning, and demonstrates that tighter coupling between information seeking and reasoning can match or exceed larger models. This suggests a path toward more efficient and interpretable retrieval-augmented systems.
- Retrieval and generation can be effectively unified at the token level, challenging the assumption that they require separate architectural components
- Models can learn to self-regulate information-seeking behavior without explicit external classifiers, improving interpretability and reducing system complexity
- Structured training data aligned with control tokens is sufficient to teach multi-step reasoning with dynamic evidence integration, suggesting a scalable supervision approach
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