ZeroSiam is an asymmetric architecture using a learnable predictor and stop-gradient that prevents collapse in test-time entropy minimization while also regularizing biased signals for improved performance.
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GLiBRL uses GLMs with learnable basis functions for exact Bayesian inference in deep BRL, derives a closed-form link between L2 task distances and kernel task similarity, and reports up to 1.8x gains over prior meta-RL on MuJoCo and MetaWorld.
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ZeroSiam: An Efficient Asymmetry for Test-Time Entropy Optimization without Collapse
ZeroSiam is an asymmetric architecture using a learnable predictor and stop-gradient that prevents collapse in test-time entropy minimization while also regularizing biased signals for improved performance.
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Generalised Linear Models in Deep Bayesian RL with Learnable Basis Functions
GLiBRL uses GLMs with learnable basis functions for exact Bayesian inference in deep BRL, derives a closed-form link between L2 task distances and kernel task similarity, and reports up to 1.8x gains over prior meta-RL on MuJoCo and MetaWorld.