Can a 8B-parameter language model produce provably valid multi-step plans instead of plausible guesses? MIT CSAIL researchers introduce PDDL-INSTRUCT, an instruction-tuning framework that couples logical chain-of-thought with external plan validation (VAL) to lift symbolic planning performance of LLMs. On PlanBench, a tuned Llama-3-8B reaches 94% valid plans on Blocksworld, with large jumps on Mystery Blocksworld and Logistics; across domains they report up to a 66% absolute improvement over baselines.

But What’s new?

The research team tackles a well-known failure mode: LLMs often generate “plausible-sounding” but logically invalid multi-step plans. PDDL-INSTRUCT couples explicit state/action semantics with ground-truth checking:

• Error education: Models are trained to explain why candidate plans fail (unsatisfied preconditions, wrong effects, frame violations, or goal not reached).
• Logical chain-of-thought (CoT): Prompts require step-by-step inference over preconditions and add/del effects, yielding state→action→state traces ⟨sᵢ, aᵢ₊₁, sᵢ₊₁⟩.
• External verification (VAL): Every step is validated with the classic VAL plan validator; feedback can be binary (valid/invalid) or detailed (which precondition/effect failed). Detailed feedback yielded the strongest gains.
• Two-stage optimization:
  • Stage-1 optimizes the reasoning chains (penalizing state-transition errors);
  • Stage-2 optimizes end-task planning accuracy.

How Good is it? Benchmarks

Evaluation follows PlanBench—Blocksworld, Mystery Blocksworld (predicate names obfuscated to break pattern-matching), and Logistics—established stress tests where generic LLMs historically underperform on plan generation. The authors highlight that Mystery Blocksworld is particularly challenging; prior studies often report <5% validity without tool support.

• Blocksworld: up to 94% valid plans with Llama-3-8B under PDDL-INSTRUCT.
• Mystery Blocksworld: large relative gains; the paper reports dramatic improvement versus a near-zero baseline (reported as orders-of-magnitude, e.g., 64× in their summary figures/tables).
• Logistics: substantial increases in valid plans.

Across domains, the research team showcase up to 66% absolute improvement over untuned baselines. Detailed validator feedback outperforms binary signals, and longer feedback budgets further help.

Summary

PDDL-INSTRUCT shows that coupling logical chain-of-thought with external plan validation can materially improve LLM planning, but its current scope is classical PDDL domains (Blocksworld, Mystery Blocksworld, Logistics) and relies on VAL as an external oracle; the reported gains—e.g., 94% valid plans on Blocksworld and large relative improvements on Mystery Blocksworld with Llama-3-8B—demonstrate a viable path for neuro-symbolic training where reasoning steps are grounded in formal semantics and checked automatically, suggesting immediate utility for agent pipelines that can tolerate a verifier in the loop while longer-horizon, temporal/numeric, and cost-sensitive planning remain open extensions.

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