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We study the problem of decomposing the response distribution of a given pretrained language model into a structured, strategy-conditioned representation. Specifically, we learn a latent-variable factorization $p_\\theta(y \\mid x) \\leadsto (r_\\phi(z \\mid x), g_\\phi(y \\mid x,z))$, where a router $r$ maps each input to a distribution over latent strategies $z$ and a generator $g$ produces the response conditioned on that strategy. A key challenge is that the generator, initialized from the base model, already represents $p_\\theta(y \\mid x)$ without using $z$. Standard variational inference therefore gives the model no incentive to route information through $z$ and can yield a severe form of posterior collapse. To address this, w","title":"Uncovering Latent Reasoning Strategies in Language Models","url":"https://arxiv.org/abs/2607.17674","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.17674v1 Announce Type: cross \nAbstract: A language model $p_\\theta(y \\mid x)$ trained on reasoning tasks learns to solve problems via multiple distinct strategies, yet these strategies are implicit and entangled within the model's response distribution. We study the problem of decomposing the response distribution of a given pretrained language model into a structured, strategy-conditioned representation. Specifically, we learn a latent-variable factorization $p_\\theta(y \\mid x) \\leadsto (r_\\phi(z \\mid x), g_\\phi(y \\mid x,z))$, where a router $r$ maps each input to a distribution over latent strategies $z$ and a generator $g$ produces the response conditioned on that strategy. A key challenge is that the generator, initialized from the base model, already represents $p_\\theta(y \\mid x)$ without using $z$. Standard variational inference therefore gives the model no incentive to route information through $z$ and can yield a severe form of posterior collapse. 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