CapTrack shows post-training causes drift beyond facts, with instruction fine-tuning producing stronger behavioral changes than preference optimization across model families.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Meta-Aligner introduces a meta-learner network that produces dynamic preference weights to enable bidirectional optimization between preferences and LLM policy responses for multi-objective alignment.
citing papers explorer
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CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training
CapTrack shows post-training causes drift beyond facts, with instruction fine-tuning producing stronger behavioral changes than preference optimization across model families.
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Meta-Aligner: Bidirectional Preference-Policy Optimization for Multi-Objective LLMs Alignment
Meta-Aligner introduces a meta-learner network that produces dynamic preference weights to enable bidirectional optimization between preferences and LLM policy responses for multi-objective alignment.