RWKV-7 "Goose" with Expressive Dynamic State Evolution — mentioned by Aedelon

Mon, 23 Feb 2026 17:30:50 GMT

Author here. The core claim: RWKV-7 (2.9B params, RNN) scores 72.8% avg across standard benchmarks vs LLaMA 3.2's 69.7% — trained on 3.1T tokens vs ~9T. Same parameter count, one-third the data. The more interesting result is architectural: RWKV-7 formally exceeds TC⁰, the complexity class bounding standard Transformers (Merrill & Sabharwal's proof in the paper). It solves state-tracking problems that fixed-depth attention provably cannot. Inference runs in O(1) memory per token — no KV cache. The hybrid variant (RWKV-X) hits 99.8% passkey retrieval at 64K and 1.37x Flash Attention v3…

Paper and discussion history. A mention is not an endorsement.

RWKV-7 "Goose" with Expressive Dynamic State Evolution — mentioned by cs702

Wed, 04 Feb 2026 00:28:22 GMT

> Who out there is working on ... infinite history? Many people are still working on improving RNNs, mostly in academia. Examples off the top of my head: * RWKV: https://arxiv.org/abs/2006.16236 / https://arxiv.org/abs/2404.05892 https://arxiv.org/abs/2305.13048 * Linear attention: https://arxiv.org/abs/2503.14456 * State space models: https://arxiv.org/abs/2312.00752 / https://arxiv.org/abs/2405.21060 * Linear RNNs: https://arxiv.org/abs/2410.01201 Industry OTOH has gone all-in on Transformers.

Paper and discussion history. A mention is not an endorsement.

RWKV-7 "Goose" with Expressive Dynamic State Evolution · HackerPapers discussions

RWKV-7 "Goose" with Expressive Dynamic State Evolution — mentioned by Aedelon

RWKV-7 "Goose" with Expressive Dynamic State Evolution — mentioned by cs702