102-ipc-blocksworld-instances-180-second-budget-classical-planner-fast CLM-0283observationsingle paperReviewed by AI
On 102 IPC Blocksworld instances with a 180-second budget, a classical planner (Fast Downward) solved 85%, a model planning directly solved 64%, and the same model choosing one action at a time against a PDDL simulator with observe, reset and retry solved 67%, a consistent but small three-point gain at about 5.7 times the token cost, so interactive simulation helps a little and classical search still wins where a formal model exists.
Capability: Planning a sequence of actions before acting
Observed on
Claude Haiku 4.5, Blocksworld only, PyPDDLEngine over MCP, 2026; uniform time budget..
Sources
- One small model and one domain; the cost-benefit ratio is the point, and the classical baseline's dominance matches PlanBench-era findings.
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Related claims
- Translating natural-language planning problems into PDDL can be learned from solver feedback alone, with one model acting as actor, judge and editor: on PlanBench average success rose from 35.5% (LLM+P) to 70.8%, with 66.3% of successes judged faithful to the described task and semantic drift, where a solvable but wrong problem is produced, cut to 6.4%.Planning a sequence of actions before acting · reviewed by AI
- Prompting with a Task-Method-Knowledge model, which spells out what to do, how, and why in a hierarchical structure, lifted o1 on the obfuscated Random Blocksworld set of PlanBench from about 32% to 97%, and o1-mini from about 9% to 27%, gains far beyond the roughly 1% that zero-shot chain-of-thought gave on PlanBench in earlier work; the smaller model did not benefit on the Mystery variant.Planning a sequence of actions before acting · reviewed by AI
- For long-horizon embodied task planning where a symbolic model defines action preconditions and effects, correcting a violated plan by edits the action model itself implies (inserting the missing navigate or open) raises plan success more per LLM call than returning the violation to the LLM for replanning, and the gain persists for frontier models as well as compact ones.Planning a sequence of actions before acting · reviewed by AI
- When a calculator-using model is trained with reinforcement learning on verifiable final-answer rewards for an arithmetic search task (Countdown), the gain lands almost entirely at low k — pass@1 rises sharply while pass@16 moves little or falls — because the update can only reinforce correct trajectories the starting policy already samples, and prompt groups with no correct sample supply no gradient.Using the tools it is given · reviewed by AI
- For LLM-guided narrow-space parking where an optimal-control solver supplies pass/fail evidence, growing a decision knowledge base offline from accumulated solver and validation failures — retaining an entry only when re-planning then yields a feasible trajectory — raises planning success from 85.1% to 94.7% and closed-loop success from 74.6% to 84.2% over an empty knowledge base, with model weights frozen.Improving its own scaffolding from failures · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Stronger models or harder domains where step-wise simulation yields a large gain over direct planning, or beats the classical baseline.