SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
arXiv:2606.07705v1 Announce Type: new Abstract: Although multi-objective reinforcement learning (MORL) is central to aligning large language models with complex human preferences, the prevailing practice of static weighted summation overlooks a more fundamental phenomenon: reward learning is markedly asynchronous across objectives. Well-learned dimensions quickly produce homogeneous, low-variance signals whose residual noise contaminates the aggregated reward (in GRPO) or occupies a fixed share of the advantage budget (in GDPO), interfering with the scarce yet high-value signals carried by und
The paper addresses a core challenge in aligning large language models, specifically the asynchronous nature of reward learning in multi-objective reinforcement learning.
This research provides a novel approach to improving the efficiency and effectiveness of training large language models to align with complex human preferences.
The proposed 'Stage-Aware Dynamic Weighting' (SAW) method offers a more sophisticated way to handle multi-objective optimization, moving beyond static weighting schemes.
- · AI researchers
- · Large language model developers
- · AI alignment research
- · Companies developing LLM-powered products
- · Developers relying on static multi-objective RL methods
Improved efficiency and accuracy in aligning large language models with diverse requirements.
Faster development and deployment of more capable and ethically aligned AI systems.
Acceleration of AI applications across various domains as models become more reliably objective-driven.
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Read at arXiv cs.LG