Self-improvement of a decoder-only transformer yields plans averaging 30% shorter than a source symbolic planner, over 80% optimal where known, with sub-exponential latency scaling.
Fikes and Nils J
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Novelty estimation via LLM prompts enables pruning in Tree-of-Thought search, reducing overall token usage on language planning benchmarks.
citing papers explorer
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Self-Improvement for Fast, High-Quality Plan Generation
Self-improvement of a decoder-only transformer yields plans averaging 30% shorter than a source symbolic planner, over 80% optimal where known, with sub-exponential latency scaling.
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Novelty-based Tree-of-Thought Search for LLM Reasoning and Planning
Novelty estimation via LLM prompts enables pruning in Tree-of-Thought search, reducing overall token usage on language planning benchmarks.