Abstract: Multi-objective reinforcement learning (MORL) is a structured approach for optimizing tasks with multiple objectives. However, it often relies on pre-defined reward functions, which can be ...

At the inaugural Columbia SPS Faculty Summit on AI Teaching and Learning, faculty and industry experts explored AI’s emerging ...

RLP uses a single network (shared parameters) to (1) sample a CoT policy 𝜋 𝜃 ( 𝑐 𝑡 ∣ 𝑥 < 𝑡 ) π θ (c t ∣x <t ) and then (2) score the next token 𝑝 𝜃 ( 𝑥 𝑡 ∣ 𝑥 < 𝑡 , 𝑐 𝑡 ) p θ (x t ∣x <t ...

What are the differences between lesson objectives, learning objectives and success criteria and how can we sharpen our lesson planning and pedagogical choices? Helen Webb offers some practical ...

Rick: A lot of parents and educators may be familiar with the phrase “mastery learning” but not have a clear idea what it means in practice. What is it exactly? Scott: My journey began in 2012 when I ...

This repo includes GMDG applied to classification, regression, and segmentation tasks. Updated 2024/11/18 Hey there, I have a reported issue: "I noticed a mismatch between the reported results for the ...

The Centre for Advanced Research Computing (ARC) of University College London (UCL) designs and delivers continuing professional development offerings in digital research practices to colleagues and ...

Does TRL support multi-objective reinforcement learning to align a large language model? For example, if we want to align a machine translation model to maximize three metrics: faithfulness, ...