I trained a 2.8B Mamba model to reason entirely in its hidden state before outputting a single token — O(1) VRAM, no KV-cache, runs on a 12GB RTX 3060

Build an open-source framework that enables training and inferencing for latent state reasoning models using Mamba architectures. This addresses the high VRAM overhead of traditional Chain-of-Thought approaches by keeping 'reasoning' in the hidden state.

Suggested repo: mamba-think

"Reasoning without the bloat: O(1) memory chain-of-thought using Mamba hidden states."

Estimated effort: 120h