arXiv:2606.24994v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for language-model reasoning can fail at both extremes of task difficulty: easy prompts often produce all-correct, low-diversity rollout groups with little gradient signal, while hard prompts can produce all-incorrect groups with no positive reward. We introduce ExTra (Exploratory Trajectory Optimization), a GRPO-compatible framework that extracts exploration signals from the model's own rollouts. ExTra combines two mechanisms: (i) a novelty reward that adds embedding-based diversity bonuses a
The continuous drive to improve large language model performance and address current limitations in reinforcement learning is leading to novel algorithmic developments.
Improving reinforcement learning for large language models can significantly enhance their reasoning capabilities, making them more effective in complex tasks and reducing reliance on extensive human-labeled data.
The proposed ExTra framework introduces a method to extract exploration signals directly from model rollouts, potentially leading to more robust and diverse training outcomes for advanced AI models.
- · AI researchers
- · Large Language Model developers
- · AI-driven applications
- · Generative AI sector
- · Developers reliant on manual prompt engineering
- · Less efficient RL techniques
Increased efficiency and effectiveness in training LLMs for complex reasoning tasks.
Accelerated development of more capable and autonomous AI agents.
Broader adoption of AI in domains requiring nuanced understanding and problem-solving, potentially disrupting more white-collar workflows.
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Read at arXiv cs.LG