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ARXIV:2605.20873 · LLM PLANNING · SUBMITTED 21 MAY · 20:27 UTC · FRESHNESS STALE
ARXIV:2605.20873LLM PLANNINGSUBMITTED 21 MAY · 20:27 UTCFRESHNESS STALEZiliang Zhao · Zenan Xu · Shuting Wang · Hongjin Qian · Yan Lei · Minda Hu · +4 at arXiv
A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following.
Opportunity summary
Pain A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable…
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequences into executable and verifiable solutions. Existing planning benchmarks, however, usually…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This limits scenario coverage, ties difficulty to surface-level proxies rather than structural sources, and offers limited support for scalable generation, automatic verification, or planning-oriented…
LLM Planning moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
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A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following.
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10.48550/arXiv.2605.20873A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following.
Abstract
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequences into executable and verifiable solutions. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable generation targets. This limits scenario coverage, ties difficulty to surface-level proxies rather than structural sources, and offers limited support for scalable generation, automatic verification, or planning-oriented training. We introduce PlanningBench, a framework for generating scalable, diverse, and verifiable planning data for both evaluation and training. PlanningBench starts from real planning scenarios and abstracts practical workflows into a structured taxonomy of more than 30 task types, subtasks, constraint families, and difficulty factors. Guided by this taxonomy, a constraint-driven synthesis pipeline instantiates self-contained planning problems with adaptive difficulty control, quality filtering, and instance-level verification checklists. This shifts planning data construction from fixed benchmark collection to controllable generation while preserving realistic task grounding. We use PlanningBench to evaluate open-source and closed-source frontier LLMs, and find that current models still struggle to produce complete solutions under coupled constraints. Beyond evaluation, reinforcement learning on verified PlanningBench data improves performance on unseen planning benchmarks and broader instruction-following tasks. Further analysis suggests that determinate or well-specified optimal solutions provide clearer reward signals and more stable training dynamics. Overall, PlanningBench provides a controllable source of planning data for diagnosing and improving generalizable planning abilities in LLMs.
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Dimensions overall score 8.0
PROBLEM
A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable generation...
METHOD
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequences into executable and verifiable solutions. Existing planning benchmarks, however, usually treat...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This limits scenario coverage, ties difficulty to surface-level proxies rather than structural sources, and offers limited support for scalable generation, automatic verification, or planning-oriented tra...
WHY NOW
LLM Planning moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable generation targets.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequences into executable and verifiable solutions. Existing planning benchmarks, however, usually treat planning data as fixed collections of instances rather than controllable generation targets.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This limits scenario coverage, ties difficulty to surface-level proxies rather than structural sources, and offers limited support for scalable generation, automatic verification, or planning-oriented training. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Planning moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for generating scalable, diverse, and verifiable planning data that improves LLM planning capabilities and instruction following.
Segment
LLM Planning
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Commercial read
8.0/10 public viability
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proof status
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