Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning
<|reference_start|>Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning: Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge here is to develop steerable language models that trade-off multiple (conflicting) objectives in a flexible and efficient manner. This paper presents Conditioned Language Policy (CLP), a general framework for finetuning language models on multiple objectives. Building on techniques from multi-task training and parameter-efficient finetuning, CLP can learn steerable models that effectively trade-off conflicting objectives at inference time. Notably, this does not require training or maintaining multiple models to achieve different trade-offs between the objectives. Through an extensive set of experiments and ablations, we show that the CLP framework learns steerable models that outperform and Pareto-dominate the current state-of-the-art approaches for multi-objective finetuning.<|reference_end|>
@article{wang2024conditional,
    title={Conditional Language Policy: A General Framework for Steerable
  Multi-Objective Finetuning},
    author={Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph
  Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey,
  Alexandre Ram'e, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr
  Ahmed, Aranyak Mehta, L'eonard Hussenot, Olivier Bachem, Edouard Leurent},
    journal={arXiv preprint arXiv:2407.15762},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15762},
    primaryClass={cs.LG cs.AI cs.CL}
}