Value Drifts: Tracing Value Alignment During LLM Post-Training
As LLMs occupy an increasingly important role in society, they are more and
more confronted with questions that require them not only to draw on their
general knowledge but also to align with certain human value systems.
Therefore, studying the alignment of LLMs with human values has become a
crucial field of inquiry. Prior work, however, mostly focuses on evaluating the
alignment of fully trained models, overlooking the training dynamics by which
models learn to express human values. In this work, we investigate how and at
which stage value alignment arises during the course of a model's
post-training. Our analysis disentangles the effects of post-training
algorithms and datasets, measuring both the magnitude and time of value drifts
during training. Experimenting with Llama-3 and Qwen-3 models of different
sizes and popular supervised fine-tuning (SFT) and preference optimization
datasets and algorithms, we find that the SFT phase generally establishes a
model's values, and subsequent preference optimization rarely re-aligns these
values. Furthermore, using a synthetic preference dataset that enables
controlled manipulation of values, we find that different preference
optimization algorithms lead to different value alignment outcomes, even when
preference data is held constant. Our findings provide actionable insights into
how values are learned during post-training and help to inform data curation,
as well as the selection of models and algorithms for preference optimization
to improve model alignment to human values.
69090797812eca10f9cc60f5
Mehar Bhatia
69090797812eca10f9cc60f6
Shravan Nayak
claimed_verified
2026-03-17T09:38:08.025Z
643ee7606d5be535d28034f6
https://cdn-avatars.huggingface.co/v1/production/uploads/643ee7606d5be535d28034f6/1F241mP_buW74byC2GAWH.jpeg
Shravan Nayak
BAJUKA
user
BAJUKA
69090797812eca10f9cc60f7
Gaurav Kamath
69090797812eca10f9cc60f8
Marius Mosbach
69090797812eca10f9cc60f9
Karolina Stańczak
69090797812eca10f9cc60fa
Vered Shwartz
69090797812eca10f9cc60fb
Siva Reddy
Value Drifts: Tracing Value Alignment During LLM Post-Training
As LLMs occupy an increasingly important role in society, they are more and
more confronted with questions that require them not only to draw on their
general knowledge but also to align with certain human value systems.
Therefore, studying the alignment of LLMs with human values has become a
crucial field of inquiry. Prior work, however, mostly focuses on evaluating the
alignment of fully trained models, overlooking the training dynamics by which
models learn to express human values. In this work, we investigate how and at
which stage value alignment arises during the course of a model's
post-training. Our analysis disentangles the effects of post-training
algorithms and datasets, measuring both the magnitude and time of value drifts
during training. Experimenting with Llama-3 and Qwen-3 models of different
sizes and popular supervised fine-tuning (SFT) and preference optimization
datasets and algorithms, we find that the SFT phase generally establishes a
model's values, and subsequent preference optimization rarely re-aligns these
values. Furthermore, using a synthetic preference dataset that enables
controlled manipulation of values, we find that different preference
optimization algorithms lead to different value alignment outcomes, even when
preference data is held constant. Our findings provide actionable insights into
how values are learned during post-training and help to inform data curation,
as well as the selection of models and algorithms for preference optimization
to improve model alignment to human values.
Research investigates how and when value alignment occurs during the post-training phase of LLMs, finding that supervised fine-tuning establishes values, while preference optimization has limited impact.
LLMs value alignment post-training supervised fine-tuning preference optimization value drifts preference datasets