New RL framework uses language for adaptive guidance; survey covers LLM distillation techniques · 2 sources…

Researchers have introduced Hierarchical Reinforcement Learning with Language Instructions (HRLLI), a novel framework that enhances reinforcement learning efficiency by dynamically selecting relevant natural language guidance during decision-making. This approach, termed "Select-to-Act," decomposes instructions into stage-specific elements, allowing a high-level policy to guide a low-level policy for adaptive action selection. Experiments on the RTFM benchmark demonstrated HRLLI's superiority over existing instruction-conditioned RL baselines. Separately, a survey reviews On-Policy Distillation (OPD) techniques for large language models, addressing the challenge of transferring frontier capabilities to smaller models and mitigating exposure bias. The survey formalizes OPD as f-divergence minimization and organizes the field by optimization targets, signal sources, and training stabilization, highlighting its connection to KL-constrained reinforcement learning. AI

IMPACT These research papers explore advanced techniques for improving AI model efficiency and capability transfer, potentially leading to more capable and deployable AI systems.

RANK_REASON Two arXiv papers detailing novel research in AI, one on hierarchical reinforcement learning and another surveying distillation techniques for LLMs.

Read on arXiv cs.AI →

• arXiv
• $f$-divergence
• imitation learning
• large-language models
• Mingyang Song
• On-Policy Distillation
• reinforcement learning
• reinforcement learning from human feedback
• Hierarchical Reinforcement Learning with Language Instructions
• RTFM benchmark
• Select-to-Act

• paper
• model release

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