Embodied CoT Distillation From LLM To Off-the-shelf Agents

Honguk Woo; Minjong Yoo; Wonje Choi; Woo Kyung Kim

REVIEW 3 major objections 6 minor 2 cited by

Embodied CoT Distillation From LLM To Off-the-shelf Agents

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read DeDer claims embodied planning can be distilled from PaLM-scale LLMs into two small models that run in under a second on off-the-shelf GPUs, outperforming the teacher-scale planner on ALFRED.

desk verdict DeDer has a sensible two-tier distillation idea, but the headline ALFRED numbers rest on an unspecified embodied KG that may be oracle state, so the strong claims should not be taken at face value. read the letter →

arxiv 2412.11499 v1 pith:HCBEPKEE submitted 2024-12-16 cs.AI cs.RO

Wonje Choi , Woo Kyung Kim , Minjong Yoo , Honguk Woo This is my paper · ORCID

classification cs.AIcs.RO

keywords embodiedAIchain-of-thoughtdistillationsmalllanguagemodelstwo-tierpolicyknowledgegraphALFREDbenchmarkzero-shotplanning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

DeDer claims that a large language model's embodied planning ability can be distilled into two very small language models that run on ordinary hardware in under a second per decision. The framework splits policy-making into a reasoning tier, which emits rationales about goals, states, observations, and sub-goals, and a planning tier, which turns those rationales into the next action. On the ALFRED household benchmark, the two small models (a T5-small reasoner plus a GPT-2 planner, about 160 million parameters in total) report higher success rates than LLM-based planners such as PaLM-540B, and higher rates than the prior distillation methods SCoTD and SCOTT. If correct, embodied agents would no longer need to query a large model at runtime, which is what would make complex planning feasible on capacity-limited, off-the-shelf devices. The load-bearing ingredient is an embodied knowledge graph of semantic triples that the reasoning tier reads before producing its rationales.

What carries the argument

The load-bearing object is the embodied knowledge graph: a growing set of semantic triples of the form (subject, relation, object), for example (Apple, On, Table) or (Agent, Pickup, Knife), which is refreshed at every step by an update function $U$ and filtered by a retriever $V$ before being presented to the reasoning policy. This graph converts the agent's interaction history and current observation into a compact prompt that a small language model can actually read. The reasoning policy is an encoder-decoder with soft prompt pools, a causal attention module, and a gated attention module, trained jointly with a rationale-reconstruction loss and a behavior-based contrastive loss defined on pairs of graphs that lead to the same action. The planning policy is a separate decoder trained to generate the next plan conditioned on the rationales, so the whole pipeline produces a plan in one step rather than iteratively.

What would settle it

Run DeDer on ALFRED feeding it raw RGB-D observations only, with the embodied knowledge graph built by a perception module trained on separate data instead of taken from simulator state. If success rates drop toward the end-to-end baseline that performs the same task without the graph, then the knowledge graph, not the distilled reasoning, is the source of the reported performance, and the claim that sLM policies can plan on off-the-shelf devices is falsified.

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Extended reading notes

Core claim

The paper's central claim is that decomposing an LLM's decision process into a two-tier hierarchy makes it possible to distill embodied planning into small language models that match or exceed the teacher-scale LLM planners. DeDer writes the policy as $\Phi_{\mathrm{sLM}} = \Phi_P \circ \Phi_R$, where the reasoning policy $\Phi_R$ (a T5-small encoder-decoder with causal and gated attention) produces a set of rationales in a single forward pass, and the planning policy $\Phi_P$ (a GPT-2 decoder) converts those rationales into an executable action plan. The authors report that DeDer-GPT2 reaches 81.8% success on seen tasks, 52.7% on tasks with randomized object layouts, and 40.3% on tasks in entirely new environments, compared with 66.8%, 33.6%, and 17.2% for LLM-planner-PaLM on the same categories, all evaluated zero-shot. They also report 0.65 seconds of inference time on an RTX 3090 and 1.16 seconds on an RTX 3050, against 9.37 seconds for an LLM-planner variant running on the same hardware class.

Load-bearing premise

The paper assumes that an embodied knowledge graph written as semantic triples such as (Apple, On, Table) is available at every planning step, but it does not describe how raw observations are turned into those triples; if that conversion needs an external perception system or ground-truth simulator state, the claimed deployment on off-the-shelf devices is not supported.

Editorial extensions

If this is right

Embodied planning can run entirely on small models: roughly 160 million parameters total, with sub-second inference on consumer GPUs, so no large model needs to be queried during task execution.
The two-tier split transfers better than end-to-end distillation: at the same GPT-2 capacity, DeDer reports 81.8% seen success versus 33.1% for the single-tier End2End baseline.
Reasoning capacity matters more than planning capacity: growing the T5 reasoner from small to large raises unseen-environment success from 40.3% to 47.7%, while growing the GPT-2 planner leaves performance essentially flat.
The reported zero-shot results in unseen environments imply that the distilled rationales carry knowledge that generalizes beyond the expert dataset's scenes, object layouts, and task descriptions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: the paper never specifies who converts raw observations into the semantic triples of the embodied knowledge graph; if the triples come from the ALFRED simulator's ground-truth state rather than from perception, the comparison against LLM planners may reward access to privileged information rather than planning skill.
Testable extension: run DeDer with the same small models but with triples produced by an off-the-shelf vision-language perception module; the drop in success rate would quantify how much of the reported performance depends on the assumption that the graph is already correct.
The contrastive loss on graph pairs is a transferable idea: forcing embeddings of graphs that lead to the same action to be close should make any compact re-planning module more stable under small environment changes, outside ALFRED as well.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear Colleague,

Quick take: DeDer is a reasonable distillation framework with a neat two-tier split, but the paper's headline numbers are not yet believable because the embodied knowledge graph—the sole perceptual input to the policy—is never derived from raw observations. If the triples come from ALFRED's simulator state, the comparison against text-only LLM planners is unfair and the "off-the-shelf" claim collapses. That's a gap, not a proven error, but it's the load-bearing one.

What's genuinely new: the separation of a reasoning-policy (T5-small) that outputs rationales from a planning-policy (GPT-2) that outputs actions, with the reasoning-policy prompted by an embodied KG and trained with contrastive attention to produce multiple rationales in one pass. The design is sensible: keep the expensive part small and structured, let the planner be a simple conditional model. The ablations show the KG and the contrastive learning each add something, and the inference-time measurements (0.65s on an RTX 3090) are concrete.

Where it wobbles: first, the KG. Equations (5)-(7) make g the only input to the reasoning-policy, but the paper never defines how an RGB observation becomes triples like (Mug, in, Sink). The example in Figure A.8 lists object classes, containment, agent position, and action history—none of which are directly readable from an egocentric frame. If U or V uses ground-truth simulator state, then every step receives privileged information that SayCan, ZSP, and LLM-planner don't, and the 40.3% Unseen Environment SR may reflect a perfect perception oracle, not distilled reasoning. This is the key thing to fix.

Second, the comparison to LLM-planner-PaLM is few-shot vs. trained; that's an apples-to-oranges headline. The same-size distillation baselines (SCoTD, SCOTT) are more relevant, but they also don't get an oracle KG, so the gap there is confounded too.

Third, Table A.14 has an impossible row: t5-small with gpt2-large reports Unseen Environment SR 67.5 and GC 39.7. GC should be at least SR if tasks are a conjunction of subgoals. Likely a typo, but it makes you wonder about the other numbers.

No code or data are released, so I can't check. These are all fixable. If the KG is actually built from vision, the authors need to describe that module and preferably release it; if it's simulator state, the claims need to be scaled way back.

Verdict: worth a serious referee. The idea has legs, the write-up is coherent, and the problem is important. But the current evidence does not support the strong claims, and the KG gap is exactly what a careful reviewer should push on.

Best.

Referee Report

3 major / 6 minor

Summary. The paper introduces DeDer, a framework for distilling embodied chain-of-thought reasoning from a large language model into two small language models: a T5-small-based reasoning-policy that generates rationales from an embodied knowledge graph, and a GPT-2-based planning-policy that maps rationales to action plans. The rationale dataset is built by prompting an LLM with MDP-featured in-context learning and self-critic filtering over 312 expert ALFRED trajectories. The reasoning-policy is trained with a rationale reconstruction loss and a behavior-based contrastive loss, while the planning-policy is trained with a plan reconstruction loss. Evaluation on ALFRED reports success rates of 81.8% (Seen), 52.7% (Unseen Spatial), and 40.3% (Unseen Environment) for DeDer-GPT2, outperforming both LLM-based planners such as LLM-planner-PaLM and prior distillation baselines SCoTD and SCOTT, with sub-second inference on RTX 3090/3050 GPUs.

Significance. If the reported results are taken at face value and the missing perception component is properly specified, the paper would make a noteworthy empirical contribution: it would show that sLM-based policies, after targeted distillation, can outperform much larger LLM planners on a complex embodied benchmark while running on off-the-shelf hardware. The two-tier decomposition, the contrastively prompted attention for concurrent rationale generation, and the self-verification pipeline for rationale dataset construction are all sensible and useful ideas. On the negative side, the paper releases no code or data, the provenance of the embodied knowledge graph is unspecified, the headline Unseen Environment result rests on only 58 tasks, and there is a numerical inconsistency between Table 5 and Table A.14. These issues currently prevent the central claim from being fully verified.

major comments (3)

[§4.2, Eqs. (5)-(7); Appendix D] The embodied KG is the sole input to the reasoning-policy Phi_R in Eq. (7), and Eq. (5) defines an update U : (g_{t-1}, a_{t-1}, o_t) -> g_t, but the paper never specifies how raw ALFRED observations (RGB-D frames) are converted into triples such as (Apple, On, Table) or (Agent, at, Sink). Appendix D lists observations as object-name lists and retrieved facts that encode agent position, containment, visibility, and action history, none of which are directly readable from an egocentric frame. If these triples come from simulator ground truth or an external perception oracle, then at every planning step DeDer receives privileged state information that the text-only LLM baselines do not receive, which would undermine both the comparison with PaLM/LLaMA2 planners and the claimed deployment on off-the-shelf devices. The authors must either describe a concrete perception-to-triple mechanism or explicitly state the assumption that such oracle information is available; without this, the central claim is unsupported.
[Table 5 vs. Table A.14] There is a direct numerical inconsistency in the capacity-scaling experiments. Table 5 reports Unseen success rate 46.1±1.1 for the t5-small + gpt2-large configuration, while Table A.14 reports 67.5±1.7 for SR and 39.7±2.0 for GC in the corresponding row. The GC value being lower than SR in that row is also internally inconsistent with the GC>=SR pattern in every other row. This discrepancy affects the conclusion in §5.3 that the reasoning-policy capacity is the main driver of generalization. The authors need to correct the numbers and ensure Tables 5 and A.14 are consistent.
[§5.1, Table 1; Unseen Environment evaluation] The headline Unseen Environment result is based on only 58 tasks, and the reported confidence intervals (±0.9 SR for DeDer-GPT2) appear to reflect variance across three training seeds only, not the variance across tasks within the category. With 58 tasks, the standard error across task sampling would be much larger. The paper should report task-level bootstrap confidence intervals or otherwise justify why the tiny reported intervals are appropriate. This is important because the central claim of superiority over SCoTD and SCOTT in the Unseen Environment column rests on differences of roughly 13-16 percentage points, which may or may not be significant under a correct statistical treatment.

minor comments (6)

[§4.2, Eq. (6) and Appendix B.4] The KG retriever threshold delta and the contrastive margin epsilon in Eq. (12) are never assigned values; only alpha is given (0.5 in Appendix B.4.2). Please report the actual values used for delta and epsilon in all experiments.
[Throughout] The method name is written inconsistently as DEDER, DeDer, and DEDER-GPT2; please standardize the notation.
[§5.3, Table 5 caption] The caption refers to 'Phi_D' but the paper defines the planning-policy as Phi_P; please correct the symbol.
[Figure 4] The x-axis labels in Figure 4 are not legible in the provided version; please add readable query indices and a clear legend.
[Appendix B.1] It is unclear whether the PaLM-based baselines (SayCan-PaLM, LLM-planner-PaLM, ZSP-PaLM) in Table 1 are from the original papers or reimplemented with the hyperparameters in Table A.7; please clarify the source of each baseline number.
[§5.1, Baselines and Table 1] The sentence 'In evaluating in off-the-shelf devices, we adopt sLMs for these language planning baselines' is ambiguous, since Table 1 also includes PaLM and LLaMA2 variants; please state clearly which baselines use which underlying model and how the affordance/grounding inputs are provided to each.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DeDer is an externally benchmarked distillation pipeline; the unspecified observation-to-KG mapping is a missing component, not a definitional reduction.

full rationale

DeDer's derivation chain is an empirical distillation pipeline. Rationales are generated by an LLM in Eq. (2), filtered by the LLM self-critic in Eq. (3), and then used as supervised targets for the reasoning-policy and planning-policy via the reconstruction losses in Eqs. (11), (12), and (14). The reported claims are evaluated against the external ALFRED benchmark using SR and GC, so task success is not defined in terms of the training objectives. The only internal validation loop is the LLM self-critique in Eq. (3), which checks whether the extracted rationale set can reproduce the expert plan; this is a data-quality filter, not a predictor of ALFRED success. The embodied KG in Eqs. (5)-(7) is the one load-bearing input whose construction from raw ALFRED observations is never specified: Appendix D shows observations as object-name lists ('Mug, Spatula, Apple, Fork') and retrieved facts such as '(Mug, in, Sink), (Agent, at, Sink), (Agent, PickedUp, Fork)', which encode object identities, locations, containment, and action history. This is a missing-component or potential privileged-state concern that affects the fairness of the comparison and the deployment claim, but it is not a circular reduction: no equation defines g from raw pixels, and no fitted parameter is renamed as a prediction. The paper's own Limitation section only discusses sLM capacity dependence and does not address this gap. The self-citation to Choi et al., 2023, cited alongside Stooke et al., 2021 and Zhang et al., 2022 for contrastive learning, is not load-bearing and does not substitute for an independent derivation. Accordingly, no circular step is exhibited.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The central claim rests on standard supervised losses, the assumption that LLM-generated and self-critic-filtered rationales are valid reasoning targets, the availability of an embodied KG with correct semantic triples at runtime, and the fairness of the rule-based executor across all methods. There are three reported free hyperparameters (alpha, delta, epsilon), and at least two of them are not given values. No new physical entities are postulated; the embodied KG is an internal data structure with only internal evidence.

free parameters (3)

alpha (attention scaling factor) = 0.5
Eq. (9), Table A.10. Chosen by hand; no sensitivity analysis is shown.
delta (KG retriever threshold) = not reported
Eq. (6). Controls which triples are included in g; the value is not given anywhere, so the exact input to the reasoning policy is under-specified.
epsilon (contrastive margin) = not reported
Eq. (12). Margin for the triplet loss; not specified in Appendix B.

assumptions (5)

domain assumption ALFRED expert transitions and self-critic-filtered rationales capture the reasoning needed for plan prediction
Section 4.1 and Eq. (3). The distillation target is generated by the LLM from expert transitions and filtered by the LLM itself; no independent verification that the rationales are sufficient or necessary.
domain assumption An embodied KG with correct semantic triples is available at each step
Section 4.2, Eqs. (5) and (6). The policy input g is assumed to be constructible from observations; the paper does not describe the perception mechanism.
domain assumption The LLM self-critic majority vote correctly identifies whether a rationale set is sufficient to infer the expert plan
Appendix B.4.1 and Eq. (3). Rationales that pass the critic are kept; if the critic is too lenient, the dataset contains rationales that do not support plan prediction.
domain assumption The rule-based executor maps generated plans to successful ALFRED environment interactions
Section 5.1. All methods are evaluated by following generated plans with a rule-based policy; details of this executor are not provided, so baseline comparisons assume identical execution quality.
standard math Standard supervised losses and attention mechanisms behave as expected
Eqs. (9), (11), (12), and (14). No formal verification; standard machine learning practice.

invented entities (1)

Embodied knowledge graph (embodied KG)
purpose: Encapsulates environment facts as triples and is updated and retrieved at each step to prompt the sLM policy
Internal component; evidence is only the paper's own ablations (Table 3), not an external falsifiable signal.

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Cite this review

Pith. "Pith review of Embodied CoT Distillation From LLM To Off-the-shelf Agents." pith.science (2026). https://pith.science/paper/HCBEPKEE

@misc{pith2026241211499,
  author       = {Pith},
  title        = {Pith review of: Embodied CoT Distillation From LLM To Off-the-shelf Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HCBEPKEE}},
  note         = {Machine review of arXiv:2412.11499}
}

read the original abstract

We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited, off-the-shelf devices. We present DeDer, a framework for decomposing and distilling the embodied reasoning capabilities from LLMs to efficient, small language model (sLM)-based policies. In DeDer, the decision-making process of LLM-based strategies is restructured into a hierarchy with a reasoning-policy and planning-policy. The reasoning-policy is distilled from the data that is generated through the embodied in-context learning and self-verification of an LLM, so it can produce effective rationales. The planning-policy, guided by the rationales, can render optimized plans efficiently. In turn, DeDer allows for adopting sLMs for both policies, deployed on off-the-shelf devices. Furthermore, to enhance the quality of intermediate rationales, specific to embodied tasks, we devise the embodied knowledge graph, and to generate multiple rationales timely through a single inference, we also use the contrastively prompted attention model. Our experiments with the ALFRED benchmark demonstrate that DeDer surpasses leading language planning and distillation approaches, indicating the applicability and efficiency of sLM-based embodied policies derived through DeDer.

Figures

Figures reproduced from arXiv: 2412.11499 by the authors.

**Figure 1.** DEDER framework with three phases: (i) In rationale dataset construction phase, the MDP-featured in-context learning and self-critic function are employed to extract rationales from the LLM; (ii) In policy distillation phase, the sLM-based policy consisting of reasoning-policy and planning-policy is trained using the extracted rationale data; (iii) In zero-shot deployment, the distilled sLM-based policy is evaluated… view at source ↗

**Figure 2.** MDP-featured in-context learning in DEDER for rationale extraction from the LLM: the examples of inputs, queries (in red), and rationales (in blue) for the desired plan are presented, wherein MDP-aligned ones are specifically emphasized. manner from dataset DRtn. We use a retriever function F : (τ, C) 7→ Ck, as described in (Karpukhin et al., 2020). It takes a transition τ from Dexp and a set of tuples C = {c1, ..… view at source ↗

**Figure 3.** Distillation procedures in DEDER: During the rationale dataset construction phase, the LLM is iteratively prompted with queries qi and rationales ri to refine in-context examples Ck through retrieval augmentation. The LLM also serves as a critic, evaluating the validity of the extracted rationales R. During the policy distillation phase, the embodied KG containing environment information as well as expert experience… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Ablation on each rationale Reasoning-policy structure [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

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Reference graph

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