---
title: "Retrieval as Generation — paper by Li et al. (2026)"
description: "The authors propose GRIP (Generation-guided Retrieval with Information Planning), which turns… 45 references, cited by 8 papers here."
authors: ["Bo Li", "Mingda Wang", "Gexiang Fang", "Shikun Zhang", "Wei Ye"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.196/"
doi: "10.18653/v1/2026.acl-long.196"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2026-retrieval-as-generation-unified-framework/"
---
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# Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning

**Authors:** [Bo Li](https://md.research.elamur.ai/free/en/authors/bo-li/), Mingda Wang, [Gexiang Fang](https://md.research.elamur.ai/free/en/authors/gexiang-fang/), [Shikun Zhang](https://md.research.elamur.ai/free/en/authors/shikun-zhang/) and [Wei Ye](https://md.research.elamur.ai/free/en/authors/wei-ye/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.196/) · [PDF](https://aclanthology.org/2026.acl-long.196.pdf) · [DOI 10.18653/v1/2026.acl-long.196](https://doi.org/10.18653/v1/2026.acl-long.196) · [OpenAlex](https://openalex.org/W7166834131)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/li-2026-retrieval-as-generation-unified-framework/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Typical RAG pipelines fetch documents once, using the initial query, and then generate from that fixed context. This fails when information needs only become clear during step-by-step reasoning. Earlier adaptive methods rely on hand-crafted uncertainty triggers or on separate controllers and multi-stage pipelines. The authors propose GRIP (Generation-guided Retrieval with Information Planning), which turns retrieval decisions into ordinary decoding. The model emits special control tokens to request evidence, write a reformulated query, report partial progress, or finish. The retriever stays external; only the decisions about using it are generated. Training data come from QA datasets and are split into four supervision types, filtered with EM and CoverEM, using BM25 over Wikipedia. GPT-4o-mini produces the multi-hop trajectories. Supervised fine-tuning is followed by reinforcement learning that rewards both answer quality and control behavior. Across five QA benchmarks, GRIP outperforms powerful retrieval-augmented baselines and performs comparably to GPT-4o, even though it is a far smaller model. It also largely preserves general abilities, which makes retrieval timing and stopping learnable and interpretable.

## Key points
- Four control tokens drive the loop: [RETRIEVE], [INTERMEDIARY], [ANSWER], [SOLVED]. The [INTERMEDIARY]→[RETRIEVE] branch continues the evidence search, and [ANSWER]→[SOLVED] ends it. The default budget is three retrieval rounds, adjustable at inference time.
- There are four supervision types: Type-α (direct answer, identified by consistent EM answers from the LLaMA3-8B-Instruct backbone), Type-β (retrieval needed), Type-γ (multi-hop planning with a new query), and Type-θ (answer completion).
- A teacher-replacement study on Type-γ data shows that open-source teachers still yield the learned retrieval policy, so the gains do not hinge on proprietary distillation. A weaker, backbone-scale teacher (LLaMA3-8B-Instruct) lowers quality more.
- General capability is mostly preserved. GRIP scores 65.73 vs 66.56 for Instruct on MMLU and 53.8 vs 54.6 on MBPP. The SFT-RAG baseline drops further, to 62.93 and 47.8.
- On CNN/DailyMail summarization judged pairwise by GPT-4o, GRIP wins 51.0% and loses 11.5% against Instruct. It is strongly preferred over SFT-RAG (90.5% wins) and R1-Searcher (59.5% wins).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang and Wei Ye. 2026. Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{li2026retrieval,
  title = {{Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning}},
  author = {Bo Li, Mingda Wang, Gexiang Fang, Shikun Zhang and Wei Ye},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.196},
  url = {https://doi.org/10.18653/v1/2026.acl-long.196}
}
```

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*Updated 2 November 2026*