Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

Q: How reproducible is "Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence"?

Estimated time to first reproduction: a few days. Risk flags: No repository-level reproducibility signals are currently available, Estimate is based on paper-only reproduction flow. No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.

Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, Kranthi Kiran GV, Jan Kocoń, Bartłomiej Koptyra, Satyapriya Krishna, Ronald McClelland, Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Stanisław Woźniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jianguo Zhu, Ruijie Zhu, Zhou, Peng, Zhu, Jian, Zhu, Rui-Jie

Published: Apr 8, 2024

No direct implementation yet

Evidence: Inferred

Domain fit: Niche / domain-specific

Verified repos: 0

No strong AI-core implementation/artifact signals were detected from current providers.

Time to first repro: a few days

2 risk flags

DOI Publisher

We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity while maintaining the inference efficiency characteristics of RNNs. We introduce a new multilingual corpus with 1.12 trillion tokens and a fast tokenizer based on greedy ...

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matching for enhanced multilinguality. We trained four Eagle models, ranging from 0.46 to 7.5 billion parameters, and two Finch models with 1.6 and 3.1 billion parameters and find that they achieve competitive performance across a wide variety of benchmarks. We release all our models on HuggingFace under the Apache 2.0 license. Models at: https://huggingface.co/RWKV Training code at: https://github.com/RWKV/RWKV-LM Inference code at: https://github.com/RWKV/ChatRWKV Time-parallel training code at: https://github.com/RWKV/RWKV-infctx-trainer

Technical details

Canonical key: doi-10.48550_arxiv.2404.05892

Cache status: Fresh

Generated at: May 31, 2026, 6:57 AM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: missing

Time to repro: a few days

2 risk flags

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.

Implementation Evidence Summary

Confidence: low

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Reproduction Risks

Estimate is based on paper-only reproduction flow

Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 65/100, grounding 58/100, status medium.

Implementation Status

No verified maintained repo

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No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
Start from related paper: ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ.
Track assumptions and missing details in an experiment log before coding.

Time to first repro: a few days

Reproduction readiness

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Time to first repro: days

Last checked: May 31, 2026

Hardware requirements

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

No verified implementation available

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Hugging Face artifacts

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Models

RWKV Matrix-Valued Finch model

Datasets

RWKV dataset Finch dataset

Spaces

RWKV demo Finch demo

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Research context

Citations

References

Tasks

Finch, Eagle, Matrix (chemical analysis), Computer Science, Physical Sciences

Methods

None detected

Domains

Mathematics, Artificial Intelligence

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