Math reasoning gains in LLMs rarely transfer to general domains; RL tuning generalizes while SFT causes forgetting and representation drift.
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GPLD applies a row-wise Jacobian penalty to DreamerV3's posterior latent distribution, producing higher sample efficiency on DeepMind Control proprioceptive tasks.
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Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoning
Math reasoning gains in LLMs rarely transfer to general domains; RL tuning generalizes while SFT causes forgetting and representation drift.
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Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
GPLD applies a row-wise Jacobian penalty to DreamerV3's posterior latent distribution, producing higher sample efficiency on DeepMind Control proprioceptive tasks.