arXiv:2605.22817v1 Announce Type: new Abstract: Language models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a variety of task-specific reward functions. Unfortunately, the standard paradigm of LLM post-training optimizes a pre-specified scalar reward, often leading current LLMs to produce low-entropy response distributions and thus to struggle at displaying the diversity that inference-time search will require. We propose Vector Policy Optimization (VPO), an RL algorithm that explic
The increasing reliance of AI systems on sophisticated inference-time search processes like AlphaEvolve necessitates better generalization and diverse outputs from language models for optimal performance.
This development addresses a key limitation of current LLMs, enabling them to generate more varied and useful responses crucial for advanced AI applications and reducing the computational overhead of generating such diversity through other means.
The optimization paradigm for LLMs is shifting from scalar reward optimization to vector policy optimization, potentially leading to more robust and adaptable AI models that generate diverse outputs inherently.
- · AI researchers and developers
- · Companies using LLMs for complex, adaptive tasks
- · Generative AI platforms
- · LLMs with low-entropy response distributions
- · Older reinforcement learning algorithms optimized for scalar rewards
Vector Policy Optimization (VPO) will improve the test-time search capabilities and generalization of language models, enhancing their utility in complex environments.
This improved diversity and adaptability could accelerate the development of more capable AI agents and intelligent systems, reducing the need for extensive human supervision in dynamic tasks.
The ability of AI to independently generate a wider range of high-quality, diverse solutions could significantly expand the domains where AI can autonomously operate, impacting white-collar workflows and research across various fields.
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Read at arXiv cs.LG