AI RESEARCH
PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models
arXiv CS.CL
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ArXi:2601.19917v2 Announce Type: replace Strategic planning is critical for multi-step reasoning, yet compact Large Language Models (LLMs) often lack the capacity to formulate global strategies, leading to error propagation in long-horizon tasks. Our analysis reveals that LLMs possess latent reasoning capabilities that can be unlocked when conditioned on explicit plans from a teacher model; however, runtime reliance on external guidance is often impractical due to latency and availability constraints.