Discovering Preference Optimization Algorithms with and for Large
  Language Models
Offline preference optimization is a key method for enhancing and controlling
the quality of Large Language Model (LLM) outputs. Typically, preference
optimization is approached as an offline supervised learning task using
manually-crafted convex loss functions. While these methods are based on
theoretical insights, they are inherently constrained by human creativity, so
the large search space of possible loss functions remains under explored. We
address this by performing LLM-driven objective discovery to automatically
discover new state-of-the-art preference optimization algorithms without
(expert) human intervention. Specifically, we iteratively prompt an LLM to
propose and implement new preference optimization loss functions based on
previously-evaluated performance metrics. This process leads to the discovery
of previously-unknown and performant preference optimization algorithms. The
best performing of these we call Discovered Preference Optimization (DiscoPOP),
a novel algorithm that adaptively blends logistic and exponential losses.
Experiments demonstrate the state-of-the-art performance of DiscoPOP and its
successful transfer to held-out tasks.
666a470e02ff2c30275f59fe
Chris Lu
claimed_verified
2024-06-13T09:31:19.745Z
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/avatars/7345beafa590d5c9d5ac80cb33a9f0ab.svg
Chris Lu
user
chrlu
666a470e02ff2c30275f59ff
Samuel Holt
admin_assigned
2024-06-13T20:19:05.864Z
63e69f04b6a40bf941e27e99
/avatars/79afae1a88068797798dfcbf34db6c45.svg
Samuel Holt
user
samuelholt
666a470e02ff2c30275f5a00
Claudio Fanconi
admin_assigned
2024-06-13T20:19:11.331Z
63a6f89de27a6dbd485be3a6
https://cdn-avatars.huggingface.co/v1/production/uploads/63a6f89de27a6dbd485be3a6/hOPR6bk0CDSWimB7owUmA.jpeg
Claudio Fanconi
user
fanconic
666a470e02ff2c30275f5a01
Alex J. Chan
admin_assigned
2024-06-13T20:19:17.217Z
636c1e4415cd58e915bc45df
https://cdn-avatars.huggingface.co/v1/production/uploads/636c1e4415cd58e915bc45df/KnPgdPe0G5ngvXaCBua6R.jpeg
Alex J. Chan
user
XanderJC
666a470e02ff2c30275f5a02
Jakob Foerster
666a470e02ff2c30275f5a03
Mihaela van der Schaar
666a470e02ff2c30275f5a04
Robert Tjarko Lange
Discovering Preference Optimization Algorithms with and for Large
  Language Models
Offline preference optimization is a key method for enhancing and controlling
the quality of Large Language Model (LLM) outputs. Typically, preference
optimization is approached as an offline supervised learning task using
manually-crafted convex loss functions. While these methods are based on
theoretical insights, they are inherently constrained by human creativity, so
the large search space of possible loss functions remains under explored. We
address this by performing LLM-driven objective discovery to automatically
discover new state-of-the-art preference optimization algorithms without
(expert) human intervention. Specifically, we iteratively prompt an LLM to
propose and implement new preference optimization loss functions based on
previously-evaluated performance metrics. This process leads to the discovery
of previously-unknown and performant preference optimization algorithms. The
best performing of these we call Discovered Preference Optimization (DiscoPOP),
a novel algorithm that adaptively blends logistic and exponential losses.
Experiments demonstrate the state-of-the-art performance of DiscoPOP and its
successful transfer to held-out tasks.
An LLM-driven method automatically discovers performant preference optimization algorithms, leading to a new algorithm called DiscoPOP that blends logistic and exponential losses.
preference optimization offline supervised learning convex loss functions objective discovery Large Language Model logistic loss exponential loss DiscoPOP