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Found 5 papers, 1 papers with code
Reward Collapse in Aligning Large Language Models
This insight allows us to derive closed-form expressions for the reward distribution associated with a set of utility functions in an asymptotic regime.
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent
A conceptually appealing approach for learning Extensive-Form Games (EFGs) is to convert them to Normal-Form Games (NFGs).
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
We then design an uncoupled no-regret algorithm that finds an $\varepsilon$-approximate $K$-EFCE within $\widetilde{\mathcal{O}}(\max_{i}X_iA_i^{K}/\varepsilon^2)$ iterations in the full feedback setting, where $X_i$ and $A_i$ are the number of information sets and actions for the $i$-th player.
Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation
We conduct experiments on three popular datasets for document-level machine translation and our model has an average improvement of 0. 91 s-BLEU over the sentence-level baseline.
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
First, we design algorithms for learning an $\epsilon$-Coarse Correlated Equilibrium (CCE) in $\widetilde{\mathcal{O}}(H^5S\max_{i\le m} A_i / \epsilon^2)$ episodes, and an $\epsilon$-Correlated Equilibrium (CE) in $\widetilde{\mathcal{O}}(H^6S\max_{i\le m} A_i^2 / \epsilon^2)$ episodes.
