Reinforcement Learning for Language Models
Practical Solutions and Value
Multi-Objective Finetuning (MOFT)
MOFT is crucial for training language models (LMs) to behave in specific ways and follow human etiquette. It addresses the limitations of single-objective finetuning (SOFT) by allowing LMs to adapt to various human preferences and uses.
Approaches to MOFT
Two main techniques for multi-reward alignment are prompt-based and parameter-based conditioning. Prompt-based methods involve custom prompts to personalize LMs based on reward weightings, while parameter-based methods use parameter-space conditioning and multi-task training.
Conditional Language Policy (CLP)
Google’s CLP framework is more adaptable and generates better responses than existing baselines. It offers a flexible approach for finetuning LMs on multiple objectives, creating adaptable models that can balance different individual rewards efficiently.
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