We dissect Francois Fleuret's Free Transformer, which injects a learned latent variable Z into autoregressive generation via a tiny CVAE-like encoder. With only one extra non-causal block, it introduces minimal overhead yet unlocks high-level planning that improves reasoning on benchmarks. We compare latent planning to explicit chain-of-thought and ponder how combining latent and explicit reasoning could unlock new capabilities.

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