Supervised learning requires a large amount of training data, limiting its application where labeled data is scarce. To compensate for data scarcity, one possible method is to utilize auxiliary tasks to provide additional supervision for the main task. Assigning and optimizing the importance weights for different auxiliary tasks remains an crucial and largely understudied research question. In this work, we propose a method to automatically reweight auxiliary tasks in order to reduce the data requirement on the main task. Specifically, we formulate the weighted likelihood function of auxiliary tasks as a surrogate prior for the main task. By adjusting the auxiliary task weights to minimize the divergence between the surrogate prior and the true prior ofthe main task, we obtain amore accurate prior estimation, achieving the goal of minimizing the required amount of training data for the main task and avoiding a costly grid search.

We model episode sessions--parts of the episode where the latent state isfixed--and propose three keymodifications toexisting meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) priorlatent conditioning.

In the classical decision-making literature, this is achieved by two interweaving processes, policyevaluation and policyimprovement (Sutton and Barto,2018).

To achieve the highest perceptual quality, state-of-the-art diffusion models are optimized with objectives that typically look very different from the maximum likelihood andtheEvidence LowerBound (ELBO) objectives.

Incontrast toLearning fromDemonstration (LfD) that involves both action and state supervision, LfO is more practical in leveraging previously inapplicable resources (e.g.

Itcomeswiththeadvantages ofVAEs, such asstable training, largesample diversity and aprincipled inference network, while having the flexibility to model a combination of continuous and discrete generative factors.

In deep reinforcement learning, policy optimization methods need to deal with issues such asfunction approximation andthereuse ofoff-policydata.

Convex Optimizationwithout Projection Steps.arXivmath.OC, August 2011.

These mechanisms typically retrieve items in a single step and are fixed after training.

However,theanalysis ofsuchdataposes challenges due to the high levels of noise, sparsity, and data scale encountered.