Selective-Advantage Entropy-Adaptive Horizon GRPO: Asymmetric Token-Level Discounting for Efficient Reinforcement Learning of Language Models
arXiv:2606.05434v1 Announce Type: new Abstract: Group Relative Policy Optimisation (GRPO) has emerged as an effective reinforcement-learning algorithm for aligning language models on reasoning tasks, but it treats every token position and every sampled rollout symmetrically. We introduce two complementary extensions: (i) Adaptive-Horizon GRPO (AH-GRPO), which weights each token's policy gradient using a cumulative entropy-based discount that reduces the effective horizon when the model is uncertain, and (ii) Selective-Advantage AH-GRPO (SA-AH-GRPO), which applies this discounting only to negat
The continuous drive to improve the efficiency and performance of large language models for complex reasoning tasks necessitates novel algorithmic approaches.
Efficient reinforcement learning of language models is critical for advancing AI capabilities, particularly in agentic systems and complex problem-solving.
New methods for asymmetrical token-level discounting improve the training efficiency and effectiveness of language models, potentially speeding up their development and deployment.
- · AI algorithm researchers
- · Language model developers
- · Companies deploying AI agents
- · Cloud computing providers
- · Inefficient RL algorithms
- · Models requiring vast computational resources
Language models become more adept at reasoning with less computational overhead.
This efficiency could enable more complex and specialized AI agents to be developed faster and at lower cost.
Accelerated development of sophisticated AI agents could significantly disrupt white-collar workflows across various industries.
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Read at arXiv cs.LG