AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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D Diagnostic Evaluation of COntent (DECO) is introduced, a criterion-independent factorisation of content that enables controlled, criterion-level evaluation of large language models and pairwise evaluation to compare model outputs across different criteria for the same input.
Online Self-Weighted Fine-Tuning is proposed, a simple method that augments SFT with online, trajectory-level weighting and offers a favorable compute-performance trade-off as a practical approach for fine-tuning small-to-medium LLMs on binary-verifiable reasoning tasks with only 2 online rollouts.
This work develops LEMUR: Learning to Align with Multi-Objective Reinforcement Learning with Preference feedback, a novel framework where an agent interactively learns from the preferences of multiple humans to learn optimal multi-objective policies.
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