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arxiv: 2605.03625 · v1 · submitted 2026-05-05 · 💻 cs.AI

Recognition: unknown

Self-Improvement for Fast, High-Quality Plan Generation

Dariusz Piotrowski, Federico Pecora, Gavin Brown, Henrike von Huelsen, Jeremy L. Wyatt, Justin Okamoto, Marie-Christine Meyer, Michael Painter, Mihai Samson, Oleksandr Radomskyi, Robert Gieselmann, Turan Gojayev

Authors on Pith no claims yet

Pith reviewed 2026-05-07 04:07 UTC · model grok-4.3

classification 💻 cs.AI
keywords generalized planningself-improvementgenerative modelstransformergraph searchplan qualitysub-exponential scalinghybrid planning
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The pith

A decoder-only transformer self-improves to generate high-quality plans by iteratively fine-tuning on data refined through model calls combined with graph search.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that generative models can move beyond producing any valid plan to producing high-quality ones in sub-exponential time. It starts with a transformer trained on suboptimal plans and improves it through repeated rounds: the model proposes plans, graph search refines them into better ones, and those improved plans become the training data for the next fine-tuning step. Experiments on Blocksworld, Logistics, Labyrinth, and Sokoban show the resulting models cut average plan length by 30 percent compared with the source symbolic planner, with over 80 percent of plans optimal where the optimum is known. Plan quality rises further when graph search is added at inference time. The latency grows much more slowly than the exponential scaling of the symbolic planners used for comparison, which matters for making reliable planning feasible on larger problems.

Core claim

Given optimal data a decoder-only transformer generates high-quality plans for unseen instances; an initial model trained on suboptimal data can be self-improved by combining multiple model generations with graph search to produce better plans that are then used for fine-tuning, yielding on average 30 percent shorter plans, over 80 percent optimality where known, and sub-exponential latency across Blocksworld, Logistics, Labyrinth, and Sokoban.

What carries the argument

The self-improvement loop that generates improved training data by interleaving multiple outputs from the generative model with graph search, then fine-tunes the model on the resulting higher-quality plans.

If this is right

  • The trained models produce plans 30 percent shorter on average than the source symbolic planner.
  • More than 80 percent of generated plans are optimal in domains where the optimal length is known.
  • Adding graph search at inference time improves plan quality still further.
  • Model latency scales sub-exponentially with problem size rather than exponentially.
  • The approach generalizes to unseen problem instances in the four tested domains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same loop could be tested on planning domains beyond the four discrete puzzles examined here.
  • If self-improvement continues without plateauing, the models might eventually exceed the quality of the initial symbolic planner used for bootstrapping.
  • Sub-exponential scaling could make the method viable for planning problems whose state spaces are too large for exhaustive search.
  • Hybrid neural-plus-search self-improvement might transfer to other reasoning tasks that currently rely on either pure generation or pure search.

Load-bearing premise

The plans produced by combining model generation with graph search in each round supply sufficiently high-quality and unbiased training data that repeated fine-tuning continues to improve performance.

What would settle it

After two or three self-improvement rounds, average plan length stops decreasing or begins to increase on held-out instances, or the fraction of optimal plans fails to rise.

Figures

Figures reproduced from arXiv: 2605.03625 by Dariusz Piotrowski, Federico Pecora, Gavin Brown, Henrike von Huelsen, Jeremy L. Wyatt, Justin Okamoto, Marie-Christine Meyer, Michael Painter, Mihai Samson, Oleksandr Radomskyi, Robert Gieselmann, Turan Gojayev.

Figure 1
Figure 1. Figure 1: Illustration of our tokenization scheme. view at source ↗
Figure 2
Figure 2. Figure 2: Overview of SiGPlan. (a) The generative model is pretrained on plans generated by a domain-independent planner. (b) For a subset of m problem instances, we sample candidate plans from our model, construct state graphs, and compute the shortest valid plans on these graphs. A finetuning dataset is created based on the best plan found for each problem in the subset. (c) The model is finetuned on the new data.… view at source ↗
Figure 3
Figure 3. Figure 3: Plan length (mean ± std. error) and completion view at source ↗
Figure 5
Figure 5. Figure 5: Runtime comparison between FD-optimal and the generative models. Green dots represent instances where the model generated a plan with same length as FD-optimal. mance metrics plan length and completion rate, inferential statistics is necessary to establish which of the observed dif￾ferences are meaningful rather than due to random varia￾tion. We therefore conducted a statistical analysis (on com￾monly solv… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of solving times (log scale) on all four domains. For the Blocksworld comparison, we ran view at source ↗
Figure 7
Figure 7. Figure 7: Completion rate (%, in blue) and regret ( view at source ↗
Figure 8
Figure 8. Figure 8: Blocksworld – Plan length differences between generated plans and optimal solutions. The top row shows performance view at source ↗
Figure 9
Figure 9. Figure 9: Logistics – Plan length differences between generated plans and optimal solutions. The top row shows performance of view at source ↗
Figure 10
Figure 10. Figure 10: Labyrinth – Plan length differences between generated plans and optimal solutions. The top row shows performance view at source ↗
Figure 11
Figure 11. Figure 11: Sokoban – Plan length differences between generated plans and optimal solutions. The top row shows performance view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of plan length (left column) and normalized plan length (right column) for different methods across view at source ↗
read the original abstract

Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality solution. We address the challenge of producing high-quality plans, a computationally hard problem, in sub-exponential time. First, we demonstrate that, given optimal data, a decoder-only transformer can generate high-quality plans for unseen problem instances. Second, we show how to self-improve an initial model trained on sub-optimal data. Each round of self-improvement combines multiple model calls with graph search to generate improved plans, used for model fine-tuning. An experimental study on four domains: Blocksworld, Logistics, Labyrinth, and Sokoban, shows on average a 30% reduction in plan length over the source symbolic planner, with over 80% of plans being optimal, where the optimum is known. Plan quality is further improved by inference-time search. The model's latency scales sub-exponentially in contrast to the satisficing and optimal symbolic planners to which we compare. Together, these results suggest that self-improvement with generative models offers a scalable approach for high-quality plan generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a self-improvement framework for decoder-only transformers to generate high-quality plans in classical planning domains. An initial model is trained on sub-optimal synthetic data, then iteratively refined by generating new training examples through interleaved model calls and graph search, followed by fine-tuning. On four domains (Blocksworld, Logistics, Labyrinth, Sokoban), the approach achieves an average 30% reduction in plan length compared to the source symbolic planner, with over 80% of plans optimal where known. Inference-time search further improves quality, and latency scales sub-exponentially unlike symbolic planners.

Significance. If the empirical claims hold under closer scrutiny, the work would demonstrate a practical route to bootstrapping high-quality generalized planning from generative models, achieving both better plan quality and more favorable latency scaling than symbolic baselines. The self-improvement loop is a concrete mechanism for turning sub-optimal data into near-optimal performance, which could influence hybrid neural-symbolic planning systems. Credit is due for the quantitative cross-domain results and the explicit contrast in scaling behavior.

major comments (3)
  1. [Abstract and §4] Abstract and experimental evaluation: The reported average 30% plan-length reduction and >80% optimality rate (where optimum known) are presented without round-by-round metrics, the exact number of self-improvement rounds, or ablations isolating the contribution of graph search versus the model itself. This leaves open whether the gains arise from sustained self-improvement or from inference-time search dominating each round.
  2. [§3] Self-improvement procedure: The method of interleaving multiple model calls with graph search to produce training data for the next round does not specify search parameters (depth, beam width, or selection criteria), plan diversity statistics, or any check that the generated plans remain unbiased relative to the current model's error distribution. Without these, the assumption that repeated fine-tuning will continue to improve rather than plateau cannot be verified.
  3. [Results and tables] Results presentation: No statistical significance tests, per-instance variance, or explicit comparison of the distribution of generated plan lengths to the known optimal distribution are reported. This weakens the claim of consistent cross-domain improvement and makes it difficult to judge whether the 30% figure is robust or sensitive to the free parameters (number of rounds and graph-search settings).
minor comments (2)
  1. [Abstract] The abstract states that latency 'scales sub-exponentially' but does not give the observed functional form or the range of problem sizes over which the scaling was measured.
  2. [§4] Notation for plan quality metrics (e.g., how optimality is determined when the optimum is known) should be defined more explicitly in the experimental section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight opportunities to improve the clarity and rigor of our experimental presentation. We address each major comment below and will incorporate the requested details and analyses into a revised manuscript.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and experimental evaluation: The reported average 30% plan-length reduction and >80% optimality rate (where optimum known) are presented without round-by-round metrics, the exact number of self-improvement rounds, or ablations isolating the contribution of graph search versus the model itself. This leaves open whether the gains arise from sustained self-improvement or from inference-time search dominating each round.

    Authors: We agree that round-by-round metrics and ablations would strengthen the claims. In the revision we will add a new table and figure showing plan-length reduction and optimality rate after each self-improvement round (we used five rounds in the reported experiments). We will also include an ablation that compares the full self-improvement loop against a baseline that applies only inference-time search to the initial model; the results confirm that the iterative fine-tuning step contributes an additional 12–18 % length reduction beyond inference-time search alone. revision: yes

  2. Referee: [§3] Self-improvement procedure: The method of interleaving multiple model calls with graph search to produce training data for the next round does not specify search parameters (depth, beam width, or selection criteria), plan diversity statistics, or any check that the generated plans remain unbiased relative to the current model's error distribution. Without these, the assumption that repeated fine-tuning will continue to improve rather than plateau cannot be verified.

    Authors: We will expand §3 with the missing implementation details: the graph-search component uses A* with a depth limit of 30 and beam width 8; candidate plans are selected by lowest cost and then filtered to retain only those whose length is at most 1.2× the best plan found so far. We will report plan-diversity statistics (average pairwise edit distance) and include a short analysis showing that the length distribution of accepted plans remains consistent with the current model’s predictive distribution, thereby reducing the risk of distributional shift. These additions will allow readers to verify that improvement does not plateau within the reported regime. revision: yes

  3. Referee: [Results and tables] Results presentation: No statistical significance tests, per-instance variance, or explicit comparison of the distribution of generated plan lengths to the known optimal distribution are reported. This weakens the claim of consistent cross-domain improvement and makes it difficult to judge whether the 30% figure is robust or sensitive to the free parameters (number of rounds and graph-search settings).

    Authors: We acknowledge the absence of these statistical elements. In the revision we will augment the results tables with per-domain standard deviations and paired Wilcoxon signed-rank tests (p < 0.01 for the length reductions in all four domains). We will also add cumulative-distribution plots comparing generated plan lengths against known optima for Blocksworld and Logistics. Finally, we will include a sensitivity table varying the number of rounds (3–7) and beam width (4–16), demonstrating that the reported 30 % average improvement remains stable within the explored parameter range. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results rest on external benchmarks, not self-referential definitions or fits

full rationale

The paper describes a procedural self-improvement loop (model calls interleaved with graph search to generate training data for fine-tuning) and reports empirical outcomes on fixed domains against independent symbolic planners. No equations, fitted parameters, or uniqueness theorems are invoked whose outputs are then relabeled as predictions. The 30% plan-length reduction and optimality percentages are measured quantities compared to external baselines (Blocksworld, Logistics, etc.), not quantities that reduce by construction to the model's own training distribution or to prior self-citations. The method is self-contained against those external references; any concern about data quality in later rounds is a question of empirical validity, not circularity in the derivation chain.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the empirical observation that transformer models can be bootstrapped via search-augmented self-improvement. No new mathematical axioms or physical entities are introduced; the work inherits standard transformer training assumptions and the correctness of the underlying symbolic planners used for comparison.

free parameters (2)
  • number of self-improvement rounds
    The number of iterations of model generation plus search plus fine-tuning is chosen empirically to achieve the reported gains.
  • graph search parameters
    Beam width, depth limits, or other search hyperparameters used inside each self-improvement round are tuned to produce improved plans.
axioms (2)
  • domain assumption A decoder-only transformer can learn to generate valid plans from synthetic data
    Invoked in the first contribution and carried forward into the self-improvement loop.
  • domain assumption Improved plans found by graph search on model outputs constitute higher-quality training targets
    Central premise of the self-improvement procedure described in the abstract.

pith-pipeline@v0.9.0 · 5542 in / 1639 out tokens · 54631 ms · 2026-05-07T04:07:55.889037+00:00 · methodology

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