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arxiv: 2606.03685 · v1 · pith:NI2EKL2Enew · submitted 2026-06-02 · 💻 cs.LG · cs.AI

A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners

Patrick Emami , Nan Qiang , Peter Graf This is my paper

Pith reviewed 2026-06-28 11:34 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords large language modelssupervised fine-tuningworld model recoveryclassical planninginterpretabilitylinear probesaction validitystate predicates
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The pith

Supervised fine-tuning on valid action sequences enables LLMs to linearly encode action validity and state predicates in internal representations.

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

The paper investigates whether LLMs fine-tuned on planning tasks recover an internal representation of the underlying world model rather than just memorizing sequences. Through a series of interpretability experiments, it tests both the models' generative behavior via output probabilities and their hidden activations using linear probes. The work finds that SFT on valid plans produces linear encodings of action validity and some predicates, that internal representations can distinguish valid from invalid actions even when probabilities do not, and that wider coverage of the state space during training improves the accuracy of the recovered model. A sympathetic reader would care because this clarifies how knowledge about structured domains gets stored inside LLMs and whether end-to-end planners are actually reasoning about states and transitions.

Core claim

Supervised fine-tuning on valid action sequences enables LLMs to linearly encode action validity and some state predicates. Models that struggle to use output probabilities for classifying action validity may still learn internal representations that separate valid from invalid actions. Broader state space coverage during fine-tuning, such as from random walk data, yields more accurate recovery of the underlying world model.

What carries the argument

Linear probes on model activations that detect encoding of action validity and state predicates, combined with analysis of output probabilities for the same distinctions.

If this is right

  • LLMs can acquire internal representations of planning constraints through SFT without direct supervision on state variables.
  • Internal activations may reveal model understanding more reliably than the probabilities the model assigns to its own outputs.
  • Collecting training data with greater state-space coverage during fine-tuning directly improves the fidelity of the recovered world model.

Where Pith is reading between the lines

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

  • If internal encodings prove stable across tasks, probing could become a practical tool for auditing or selecting planning datasets before full training.
  • The gap between internal separation and output probabilities suggests that hybrid systems might combine probed representations with generative decoding for verification.
  • The same linear-probe approach could be applied to other structured domains such as code generation or theorem proving to test whether SFT induces similar world-model-like encodings.

Load-bearing premise

The selected interpretability techniques capture world-model recovery comprehensively without missing non-linear representations or alternative reasoning paths.

What would settle it

A finding that linear probes on activations achieve near-chance accuracy on validity prediction while the model still generates correct plans at high rates, or that random-walk training data produces no measurable improvement in probe accuracy over narrower datasets.

Figures

Figures reproduced from arXiv: 2606.03685 by Nan Qiang, Patrick Emami, Peter Graf.

Figure 1
Figure 1. Figure 1: Overview of our interpretability experiments examining world model recovery in supervised fine [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A toy example that illustrates how plans for supervised fine-tuning are generated by the three [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: To compute our world model recovery metrics, we extract activations and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Action validity linear probe results. We train linear probes on internal representations of Plansformers to predict the validity of next actions from an intermediate plan step. Each line plot shows mean and 95% confidence intervals across three training seeds. (a) and (c) are models fine-tuned without State-CoT, whereas (b) and (d) use State-CoT. Across all fine-tuning configurations, probe F1 scores reach… view at source ↗
Figure 5
Figure 5. Figure 5: State predicate linear probe results. We predict the truth values of state predicates at intermediate plan steps using linear probes trained on Plansformer activations without State-CoT. The Plansformers are fine-tuned on uniform random walk data. Probe scores for individual predicates are aver￾aged over objects for presentation clarity. The scores vary widely by predicate, e.g., in Blocksworld (5a) the (a… view at source ↗
Figure 6
Figure 6. Figure 6: Kernel density estimates for valid actions. Each data point in the density estimate is the average over a valid action’s token log probabilities. Note that we only include alternative actions to the greedily decoded action in these density estimates; models always assigned ∼ 0 log probability to the greedily decoded action tokens. We combined Blocksworld and Logistics results to create each density estimat… view at source ↗
Figure 7
Figure 7. Figure 7: ID vs. OOD kernel density estimates for valid actions: Out-of-distribution Blocksworld planning instances with 6-10 blocks, compared across training data used for fine-tuning (a-c). We overlay the ID and OOD KDE plots for valid actions. Plansformers fine-tuned on random walk data (7a) show a collapse (leftward shift) in their ability to distinguish between valid and invalid actions by token probabilities. … view at source ↗
Figure 8
Figure 8. Figure 8: Out-of-distribution action validity linear probes. Testing on Blocksworld instances with 6-10 blocks. The internal representation quality is main￾tained on OOD plans [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
read the original abstract

Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and reason about the planning problems they are solving? Due to the relative complexity of classical planning problems and the challenge that end-to-end plan generation poses for LLMs, it has been difficult to explore this question. In our work, we devise and perform a series of interpretability experiments that holistically interrogate world model recovery by examining both internal representations and generative capabilities of fine-tuned LLMs. We find that: a) Supervised fine-tuning on valid action sequences enables LLMs to linearly encode action validity and some state predicates. b) Models that struggle to use output probabilities for classifying action validity may still learn internal representations that separate valid from invalid actions. c) Broader state space coverage during fine-tuning, such as from random walk data, yields more accurate recovery of the underlying world model. In summary, this work contributes a recipe for applying interpretability techniques to planning LLMs and generates insights that shed light on open questions about how knowledge is represented in LLMs.

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

1 major / 1 minor

Summary. The manuscript presents an empirical interpretability study examining whether supervised fine-tuning (SFT) on valid action sequences enables LLMs to recover world models for classical planning. Using linear probes on internal activations and analysis of output probabilities, the authors report three findings: (a) SFT produces linear encodings of action validity and some state predicates; (b) internal representations can separate valid from invalid actions even when output probabilities are ineffective for classification; (c) broader state-space coverage during fine-tuning (e.g., random-walk data) yields more accurate world-model recovery. The work also supplies a methodological recipe for applying interpretability techniques to planning LLMs.

Significance. If the results hold after addressing causal questions, the paper would contribute useful mechanistic insight into how SFT shapes internal representations in LLM planners, moving beyond end-to-end accuracy to questions of world-model recovery. The dual focus on probes and generative behavior, together with the coverage finding, offers practical guidance for data curation. The study is grounded in standard linear-probe methodology and addresses a timely question in LLM planning research.

major comments (1)
  1. [Abstract and interpretability experiments] Abstract and interpretability experiments: The central claim that SFT enables 'recovery of the underlying world model' is supported only by evidence of linear separability. No activation interventions, direction ablations, or causal tests (e.g., patching the probe directions and measuring downstream planning accuracy) are described, leaving open whether the observed encodings are used by the model or are epiphenomenal.
minor comments (1)
  1. [Abstract] Abstract: Key experimental details (model sizes, planning domains, number of trajectories, statistical tests) are absent, making it difficult for readers to gauge the scope and robustness of the reported findings.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and the opportunity to clarify the scope of our claims. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract and interpretability experiments] Abstract and interpretability experiments: The central claim that SFT enables 'recovery of the underlying world model' is supported only by evidence of linear separability. No activation interventions, direction ablations, or causal tests (e.g., patching the probe directions and measuring downstream planning accuracy) are described, leaving open whether the observed encodings are used by the model or are epiphenomenal.

    Authors: We agree that the evidence is correlational, relying on linear probes for action validity and state predicates together with analysis of output probabilities and planning performance. The manuscript does not include activation interventions or patching experiments. We will revise the abstract, introduction, and conclusion to replace the phrasing 'recovery of the underlying world model' with 'linearly decodable components of a world model' and add an explicit limitations paragraph noting the absence of causal tests. These changes preserve the reported findings on probe accuracy, the gap between internal representations and output probabilities, and the coverage effect while accurately bounding the strength of the claims. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical interpretability study with no derivations or self-referential predictions

full rationale

This is an empirical paper that fine-tunes LLMs on planning data and applies linear probes plus output-probability analysis to inspect representations. No equations, predictions, or derivations are present that could reduce to fitted parameters or self-citations by construction. All claims rest on direct experimental measurements (probe accuracies, separability metrics) that are independently falsifiable from the training data itself. The work is self-contained against external benchmarks and contains no load-bearing self-citation chains or ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work is empirical and introduces no new mathematical entities or free parameters central to a derivation. It relies on the standard assumption that linear probes extract meaningful information from LLM activations.

axioms (1)
  • domain assumption Linear probes on internal activations can reveal whether the model encodes action validity and state predicates
    The central findings rest on the validity of linear probing as a measurement tool for internal representations.

pith-pipeline@v0.9.1-grok · 5727 in / 1204 out tokens · 66535 ms · 2026-06-28T11:34:27.296732+00:00 · methodology

discussion (0)

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Reference graph

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