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REVIEW 2 major objections 7 minor 1 cited by

Enhancing Visual Planning with Auxiliary Tasks and Multi-token Prediction

T0 review · 2 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that a 7B-parameter video-language model, trained on auxiliary goal tasks and multi-token prediction, achieves state-of-the-art visual planning results on COIN and CrossTask and is competitive on Ego4D long-term action…

desk verdict A solid, practical MLLM recipe for visual planning; the GMA leak is real but tested, and the paper deserves a conditional accept with variance checks. read the letter →

arxiv 2507.15130 v1 pith:SVFN2ZJZ submitted 2025-07-20 cs.CV

Ce Zhang , Yale Song , Ruta Desai , Michael Louis Iuzzolino , Joseph Tighe , Gedas Bertasius , Satwik Kottur This is my paper
classification cs.CV
keywords visualplanningforassistancemulti-tokenpredictionauxiliarytaskaugmentationmultimodallargelanguagemodelsinstructionalvideoslong-termactionanticipationgoalLoRA
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

The paper argues that a multimodal language model can be made into a much stronger long-horizon visual planner without new data collection by training on auxiliary tasks that reuse existing action annotations, such as goal prediction and goal-modality variation, and by training with multi-token prediction, in which extra heads force the model to predict several future tokens at once. The resulting system, VideoPlan, uses a frozen visual encoder, a 7-billion-parameter LLM, and LoRA fine-tuning, and it reaches state-of-the-art success rates on the COIN and CrossTask visual planning benchmarks, beating the next-best method by 7.3 and 3.4 percentage points when predicting the next three actions. It also performs competitively on Ego4D long-term action anticipation without egocentric pretraining. These results matter because they suggest the bottleneck for visual planning is as much the training objective and data usage as model scale.

What carries the argument

Two mechanisms carry the argument. Auxiliary Task Augmentation builds extra training tasks from existing annotations: Goal Prediction asks the model to output the user's goal given the observation as video, image, or generated text of object states, and Goal Modality Augmentation changes the goal input from text to an image or removes it entirely. Multi-Token Prediction adds K parallel output heads, with K=4 in the paper, that each predict a future token conditioned on the same hidden state; the paper's variant reuses the frozen pre-trained unembedding matrix as each head and differentiates heads only by LoRA adapters, adding 11M parameters instead of the 80M of the original linear-layer design. At inference the extra heads are discarded and the model generates autoregressively, so MTP acts as a regularizer that makes the trunk reason about future tokens without changing the final decoding procedure.

What would settle it

Train a VideoPlan variant whose Goal Modality Augmentation goal image is the last observed frame before the first future action instead of the final frame of the final future action, keeping all other data and settings fixed; if the COIN T=3 success rate does not drop below 29.1%, the paper's claimed transfer from future-state goals is called into question, and if it drops sharply the reported GMA gain is leakage rather than generalizable goal understanding.

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Extended reading notes

Core claim

The paper's central claim is that two modifications to standard MLLM training address the two obstacles that keep large models from planning well in instructional videos. Auxiliary Task Augmentation converts the same annotations into extra supervision: the model must predict the user's goal from video, image, or text observations, and it must plan from goals given in different modalities, including a goal image that is the final frame of the last future action. Multi-Token Prediction adds extra decoded heads that predict tokens one, two, three, and four positions ahead during training, so the shared trunk learns to treat the closed action vocabulary as a structured sequence rather than free-form language. VideoPlan combines both and reports 29.1% success rate on COIN and 15.4% on CrossTask for predicting three future actions, the highest among compared methods; the authors attribute the gains to the two mechanisms, not to the visual encoder, since the same-gain pattern holds when the encoder is held fixed.

Load-bearing premise

The Goal Modality Augmentation task trains the model with the final frame of the last future action as the goal image, and at test time that frame does not exist; the paper assumes the model learns transferable goal knowledge rather than exploiting the leaked future frame.

Editorial extensions

If this is right

  • On COIN, predicting three future actions, VideoPlan reaches 29.1% success rate, 7.3 percentage points above the prior best; with the same visual encoder as the prior best, the gain is 3.8 points.
  • On CrossTask, the model reaches 15.4% success rate for three-step planning, 3.4 points above the prior best, and again improves across all metrics when the visual encoder is fixed.
  • Ablation results indicate that Auxiliary Task Augmentation and Multi-Token Prediction are additive: each improves the T=3 baseline success rate alone, and together they add 3.4 points to the full model.
  • Replacing MTP's linear heads with the frozen-unembedding-plus-LoRA heads improves all metrics while cutting head parameters from 80M to 11M.
  • On Ego4D long-term action anticipation, VideoPlan posts the best verb edit distance among the compared methods and is competitive on noun and action edit distance despite not being pretrained on egocentric video.

Reading between the lines

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

  • A testable extension the paper does not run: replace the Goal Modality Augmentation goal image with a frame available at inference time, such as the last observed frame, to separate genuine goal comprehension from leakage of the future final frame; this would settle whether GMA's 1.0-point gain transfers.
  • If MTP's advantage comes from weighting consequential token transitions, the same unembedding-matrix-plus-LoRA head design should transfer to other small-vocabulary structured generation tasks, such as constrained tool-use or recipe generation, where multi-token heads are cheap to add; this is an editorial extrapolation.
  • Because VideoPlan also works on Ego4D LTA where no goal text is given, a natural decomposition is to ablate the goal on VPA; if success rate holds without it, the model's gain may owe more to temporal structure modeling than to goal conditioning.
  • The fact that a 7B model beats 70B baselines suggests scale is not the main driver in this low-data regime; testing VideoPlan on a larger closed action vocabulary would show whether the advantage persists as the label space grows.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 7 minor

Summary. The paper proposes VideoPlan, a multimodal large language model for Visual Planning for Assistance (VPA) and Long-term Action Anticipation (LTA). To address the scarcity of procedural annotations, it introduces Auxiliary Task Augmentation (ATA) with two task types: Goal Modality Augmentation (GMA), which varies how the goal is specified (text, image, or discarded), and Goal Prediction (GP). To better capture the structured action space, it adapts Multi-token Prediction (MTP) by reusing the pretrained unembedding matrix with per-head LoRA modules. The model is trained in three stages: feature alignment, auxiliary-task pretraining, and primary-task finetuning. Experiments on COIN and CrossTask report state-of-the-art Success Rate (e.g., +7.3% at T=3 on COIN with a MetaCLIP encoder), and on Ego4D LTA the method is competitive without egocentric pretraining. The paper includes same-encoder comparisons and ablations on ATA, MTP, head architecture, and partial MTP.

Significance. If the results hold, the paper offers a practical and efficient recipe for improving MLLM-based planners under limited supervision: the final model uses a 7B LLM whereas prior VPA methods use 70B models, and the same-encoder comparisons (VideoCLIP variants) partially isolate the benefit of the proposed training strategies. The ablation suite is well structured, covering each auxiliary task, the MTP head design, and a partial-MTP control for the role of cross-action token transitions. The main caveats are that the GMA auxiliary task trains on the future goal frame, which is unavailable at test time, and that all results are single runs without error bars, leaving small margins (e.g., 1.0% on CrossTask same-encoder T=4) of unknown reliability.

major comments (2)
  1. [Section 3.3, Appendix 7.3, Table 6] The GMA auxiliary task constructs the goal image as the last frame of the last future action segment to be predicted, as stated in Appendix 7.3. During standard VPA inference the goal is provided only as text, so the model never receives this future frame. The ablation in Table 6 attributes 1.0% SR (T=3) and 0.9% SR (T=4) on COIN to GMA, but the paper does not rule out that this gain is a training-time shortcut from seeing the exact future visual state. The post-hoc explanation that GMA teaches cross-modal goal dependencies is plausible but untested. Please add a control experiment in which the goal image is replaced by a frame that is not the true future state, e.g., the last frame of the input observation or a frame from a different clip, and report whether the GMA gain persists. If feasible, also compare text-goal versus oracle-image-goal test performance to diagnose reliance on the image.
  2. [Tables 2-8] All experimental results are reported from a single run, without error bars, confidence intervals, or significance tests. Several key comparisons are small: the same-encoder CrossTask T=4 SR margin over VidAssist is 1.0% (Table 3), and the GMA ablation drop is 1.0% (Table 6). These differences could plausibly be within run-to-run noise, which matters for the claims that GMA helps and that VideoPlan outperforms prior methods on that setting. Please report means and standard deviations over at least three seeds for the main comparisons and ablations, and state the number of seeds used.
minor comments (7)
  1. [Section 4.2] The text says VideoPlan achieves +7.4% and +5.7% higher Success Rate than VidAssist on COIN, but Table 2 shows +7.3% and +6.7%. Please correct the numbers.
  2. [Section 4.3 (Role of MTP)] The text states that MTP outperforms partial-MTP by 3.9% and 1.2% in Success Rate, but Table 8 shows differences of 1.0% (T=3) and 0.5% (T=4). Please correct the textual numbers to match the table.
  3. [Abstract and Conclusion] The headline gains of 7.3% and 3.4% are obtained with the MetaCLIP visual encoder, which is not the encoder used by the prior methods compared. Although the same-encoder VideoCLIP comparisons appear in Section 4.2, the abstract and conclusion should explicitly state that the headline margins use the stronger encoder, so that the method-only contribution is not overstated.
  4. [Section 4.3 (Auxiliary Task Analysis)] The cross-modal goal dependency explanation for GMA is presented as a hypothesis without supporting evidence; please label it as a hypothesis and connect it to the proposed control experiment from the major comment.
  5. [Section 3.4, Table 7] The head architecture comparison would be more interpretable if the LoRA rank and alpha for the MTP heads were specified separately from the backbone LoRA configuration, since the parameter count advantage depends on this choice.
  6. [Section 7.1] The sentence "We train our model for 1 epoch with a batch size of 1024" is ambiguous because the next sentences distinguish the auxiliary pretraining stage (1 epoch) from the VPA finetuning stage (4 epochs). Please rephrase to make the stage reference explicit.
  7. [Section 3.4, Eq. (3)] The notation is inconsistent: Eq. (1)-(2) use lowercase x_t, while Eq. (3) uses uppercase X_t. Please unify the notation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's SOTA claims are empirical results from training and evaluation, not derivations that reduce to fitted inputs or self-citations.

full rationale

I walked the claimed derivation chain and found no step where a prediction or first-principles result is equivalent to its inputs by construction. The main components are Auxiliary Task Augmentation (GMA and GP) and Multi-token Prediction; both are empirical training strategies evaluated with ablations on COIN and CrossTask. GMA uses the last frame of the last future action segment as a goal image during auxiliary training (Section 7.3), which is a potential information-leak/shortcut concern for the 1.0% SR ablation gain, but the paper does not define any measured result in terms of that frame, and the central VPA evaluation uses text goals only; this is a validity risk, not circularity. The MTP head reuses the unembedding matrix with LoRA; this is an architectural modification with an independent head-architecture ablation (Table 7). The paper cites prior work with overlapping authors (VidAssist, VLaMP), but only as external benchmarks, and the comparisons are straightforward and not used to justify the paper's premises. No fitted parameter is renamed as a prediction, and no uniqueness or ansatz is imported via self-citation. The paper is self-contained against external benchmarks, so the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The main free parameters are standard training hyperparameters and design choices, not parameters fit to the test data. The key axioms are the transferability of MTP to low-data planning, the reliability of Sentence-BERT matching, and the transferability of the goal-image auxiliary task, the last of which is a potential weakness.

free parameters (3)
  • number of multi-token prediction heads = 4
    The paper uses 4 additional heads during training (Section 3.6). This is a hyperparameter chosen by the authors, not optimized across a range in the paper.
  • LoRA rank = 64
    LoRA r=64 and alpha=128 (Appendix 7.1). These are standard choices for LLM fine-tuning and are not fitted to a specific objective.
  • frames sampled per video = 100
    Uniformly sample 100 frames at 0.5 FPS (Section 3.6). This is a fixed design choice.
assumptions (4)
  • domain assumption Multi-token prediction improves long-horizon generation in language models
    The paper relies on Gloeckle et al. [9] for the benefit of MTP; this is assumed to transfer to visual planning.
  • domain assumption Sentence-BERT embeddings reliably match free-form action text to the closed action vocabulary
    Evaluation in Appendix 7.2 uses Sentence-BERT cosine similarity to map outputs, following prior work [14,15,26].
  • domain assumption The visual encoder features (MetaCLIP or VideoCLIP) capture sufficient state information for planning
    The visual encoder is frozen and only an adapter is trained; this presumes the pretrained features are informative.
  • ad hoc to paper Training with a goal image of the final state transfers to text-only goal inference
    GMA task in Section 3.3 leaks the final frame; the paper assumes this improves generalization despite the modality shift at test time.

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Cite this review

Pith. "Pith review of Enhancing Visual Planning with Auxiliary Tasks and Multi-token Prediction." pith.science (2026). https://pith.science/paper/SVFN2ZJZ

@misc{pith2026250715130,
  author       = {Pith},
  title        = {Pith review of: Enhancing Visual Planning with Auxiliary Tasks and Multi-token Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SVFN2ZJZ}},
  note         = {Machine review of arXiv:2507.15130}
}
read the original abstract

Visual Planning for Assistance (VPA) aims to predict a sequence of user actions required to achieve a specified goal based on a video showing the user's progress. Although recent advances in multimodal large language models (MLLMs) have shown promising results in video understanding, long-horizon visual planning remains a challenging problem. We identify two challenges in training large MLLMs for video-based planning tasks: (1) scarcity of procedural annotations, limiting the model's ability to learn procedural task dynamics effectively, and (2) inefficiency of next-token prediction objective to explicitly capture the structured action space for visual planning when compared to free-form, natural language. To tackle data scarcity, we introduce Auxiliary Task Augmentation. We design and train our model on auxiliary tasks relevant to long-horizon video-based planning (e.g., goal prediction) to augment the model's planning ability. To more explicitly model the structured action space unique to visual planning tasks, we leverage Multi-token Prediction, extending traditional next-token prediction by using multiple heads to predict multiple future tokens during training. Our approach, VideoPlan, achieves state-of-the-art VPA performance on the COIN and CrossTask datasets, surpassing prior methods by 7.3% and 3.4%, respectively, when predicting 3 future actions. We further extend our method to the challenging Ego4D Long-term Action Anticipation task, and show that it is on par with the state-of-the-art approaches despite not using specialized egocentric features. Code will be made available.

Figures

Figures reproduced from arXiv: 2507.15130 by the authors.

Figure 1
Figure 1. (a) Visual Planning for Assistance (VPA): predict a se￾quence of future actions (grey) given a video observation of user’s progress and a succinct goal in text (green). (b) Auxiliary Task Augmentation: Construct additional tasks related to long-term vi￾sual planning. Inputs (green) and outputs (grey) for a given auxil￾iary tasks are connected via same colored arrows. (c) Multi-token Prediction: Extend next-token pre… view at source ↗
Figure 2
Figure 2. Our three-stage training pipeline. Stage 1 aligns the features of the visual encoder with the LLM embedding space by only training a visual adapter. Stage 2 helps the model better learn visual planning dynamics by training on other related auxiliary tasks. Finally, Stage 3 finetunes the model on VPA, the desired task at hand. ning tasks, these works typically assume the agent can in￾teract with the environment. Inst… view at source ↗
Figure 3
Figure 3. Different Head Architecture for Multi-token Predic￾tion. Top: The original MTP [9] introduces additional linear lay￾ers as the heads and shares the unembedding matrix on top of each head. Bottom: We reuse the unembedding matrix as the heads. During training, we initialize each unembedding matrix with the same pre-trained weights but add different LoRA modules. where f is the shared backbone and hi is the i-th output… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Token prediction schemes operating on a sequence of [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Success Cases and Failure Cases from COIN Dataset. The red text denotes wrong predictions. The blue text denotes repetitive action annotations in the dataset. Top: One success case of our method. Our model correctly predicts all future actions. Bottom: One failure case…
Figure 6
Figure 6. Figure 6: Effects of Auxiliary Task Augmentation (ATA) and Multi-token Prediction (MTP). The red text denotes wrong predictions. Top: Our method without ATA and MTP. The model mistakenly outputs the action “jack up the car” when predicting the second action. Even though the thir…
Figure 7
Figure 7. Figure 7: Qualitative Results for Ego4D LTA. Predicting long-term future actions are extremely challenging because the future is uncertain and there are multiple possible future action sequences. The action sequences produced by our method generally matches the person’s goal and…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.