4 accepted papers
Modern AI agents such as large language models are trained on diverse tasks---translation, code generation, mathematical reasoning, and text prediction---simultaneously. A key question is to quantify how each individual training task influences performance on a target task, a problem we refer to as…
We study the problem of efficiently estimating policies that simultaneously optimize multiple objectives in reinforcement learning (RL). Given n objectives (or tasks), we seek the optimal partition of these objectives into k groups, which is much smaller than n, where each group comprises related ob
Quantization-aware training is widely used for language model quantization in sub-4-bit precision, by training full-precision weights with gradients computed on the quantized model. The main bottleneck for this training approach is its slow convergence and plateauing of test performance, which gets …
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of n examples, how can we quickly select k out of n to best serve as the conditioning for downstream inference? This problem has broad applications in prompt tuning and chain-of-th