---
title: "RECKONING: Reasoning through Dynamic Knowledge Encoding"
description: "Language models that answer questions by reading supporting facts in their input… 70.9% accuracy. 85 references, cited by 13 papers here."
authors: ["Zeming Chen", "Gail Weiss", "Eric Mitchell", "Asli Celikyilmaz", "Antoine Bosselut"]
year: 2023
venue: "arXiv:2305.06349"
source: "https://arxiv.org/abs/2305.06349"
doi: "10.48550/arxiv.2305.06349"
arxiv: "2305.06349"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2023-reckoning-reasoning-through-dynamic-knowledge/"
---
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# RECKONING: Reasoning through Dynamic Knowledge Encoding

**Authors:** [Zeming Chen](https://md.research.elamur.ai/free/en/authors/zeming-chen/), [Gail Weiss](https://md.research.elamur.ai/free/en/authors/gail-weiss/), [Eric Mitchell](https://md.research.elamur.ai/free/en/authors/eric-mitchell/), [Asli Celikyilmaz](https://md.research.elamur.ai/free/en/authors/asli-celikyilmaz/) and [Antoine Bosselut](https://md.research.elamur.ai/free/en/authors/antoine-bosselut/) · **Year:** 2023 · **Venue:** [arXiv:2305.06349](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/) ([NeurIPS 2023](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2023/)) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2305.06349) · [PDF](https://arxiv.org/pdf/2305.06349) · [DOI 10.48550/arxiv.2305.06349](https://doi.org/10.48550/arxiv.2305.06349) · [arXiv 2305.06349](https://arxiv.org/abs/2305.06349) · [OpenAlex](https://openalex.org/W7133221460)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · 13 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Language models that answer questions by reading supporting facts in their input (in-context reasoning, ICR) degrade when that context includes facts unrelated to the question. A fine-tuned GPT-2, for example, drops from 99.4% to 70.9% accuracy once such distractors are added. RECKONING addresses this by moving the facts out of the prompt and into the model's weights. At inference time, a copy of meta-learned parameters takes a few gradient steps on a causal language modeling loss over the fact set, and the adapted model then receives only the question. Training is bi-level. The inner loop performs this knowledge-encoding adaptation, and the outer loop tunes the starting parameters so the adapted model answers correctly. An auxiliary outer-loop objective also requires the model to reproduce the facts. On ProofWriter, CLUTRR-SG and the real-world FOLIO, RECKONING outperforms a fine-tuned ICR baseline built on the same model by up to 4.5%. It also handles distractors and longer unseen reasoning chains better, and it saves compute when many questions share one knowledge set. This suggests that writing knowledge into parameters is a robust alternative to packing it into the context.

## Key points
- Name: RECKONING (REasoning through dynamiC KnOwledge eNcodING), by Zeming Chen, Gail Weiss, Eric Mitchell, Asli Celikyilmaz and Antoine Bosselut (EPFL, Stanford, Meta AI), published at NeurIPS 2023.
- Method: the inner loop runs N gradient steps of a CLM loss on the facts K. The outer loop combines cross-entropy on the answer with a knowledge-recovery CLM loss, which improved reasoning performance.
- Motivation: when distractors were added, a fine-tuned GPT-2 ICR model fell from 99.4% to 70.9% accuracy on the same questions.
- Benchmarks: ProofWriter and CLUTRR-SG (synthetic multi-hop) and FOLIO (real-world). RECKONING improved over the FT-ICR baseline by up to 4.5%.
- Trade-off: encoding new knowledge is slower than a single ICR forward pass. Because the facts are encoded only once, RECKONING becomes more efficient when several questions target the same knowledge.

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

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Zeming Chen, Gail Weiss, Eric Mitchell, Asli Celikyilmaz and Antoine Bosselut. 2023. RECKONING: Reasoning through Dynamic Knowledge Encoding. arXiv:2305.06349.

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

```bibtex
@misc{chen2023reckoning,
  title = {{RECKONING: Reasoning through Dynamic Knowledge Encoding}},
  author = {Zeming Chen, Gail Weiss, Eric Mitchell, Asli Celikyilmaz and Antoine Bosselut},
  year = {2023},
  doi = {10.48550/arxiv.2305.06349},
  eprint = {2305.06349},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.06349}
}
```

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*Updated 30 October 2026*