arXiv:2605.30789v1 Announce Type: new Abstract: We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies primarily increase diversity by injecting more token-level randomness, which may introduce step-wise noise and lead to incoherent trajectories. We uncover that smaller models within the same model family inherently exhibit higher policy-level diversity, indicated by their superior pass@k relative to larger counterparts as sample counts increase. Unlike token-level noise, th
This research provides a novel approach to enhancing LLM diversity, moving beyond token-level randomness to intrinsic model characteristics, indicating a maturing understanding of LLM optimization.
This finding suggests that smaller models within an LLM family can achieve superior performance in specific contexts (e.g., sample efficiency), potentially altering current strategies for model deployment and optimization.
The understanding of model diversity within GRPO shifts from injecting token-level noise to leveraging inherent policy-level differences in models of varying sizes, potentially leading to more efficient and coherent AI trajectories.
- · AI researchers
- · Developers optimizing LLM performance
- · Organizations with constrained compute resources
- · Strategies relying solely on token-level diversity
- · Developers solely focused on larger models for all tasks
Research into intrinsic model characteristics for diversity will accelerate.
Smaller, specialized models might gain more prominence in multi-model AI architectures.
Resource-constrained entities could achieve competitive AI performance with optimized smaller models, impacting the compute supply chain dynamics.
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Read at arXiv cs.LG