Writing study: Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
← ICML 2026 oral writing corpus
Huihan Liu, Changyeon Kim, Bo Liu, Minghuan Liu, Yuke Zhu · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Empirical analysis, diagnosis & auditing (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 22 physical pages
- Before references / appendix
- ≈ 9.69 page-equivalents
- Main figures / tables
- 8 / 5
- Attached appendix figures / tables
- 6 / 7
- Attached appendix span
- ≈ 8.92 page-equivalents
Source-bound correction: Include the actual p14 appendix contents cover and complete wrapped main headings. Remove a p15 in-prose reference to main Figure1 that was counted as a new appendix figure.
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 2 | ≈ 0.64 pp. | 2.91% |
| 2. Related Work | 2 | ≈ 0.57 pp. | 2.59% |
| 3. Preliminaries | 3 | ≈ 0.55 pp. | 2.5% |
| 4. VLAs are Surprisingly Resistant to Forgetting | 3 | ≈ 1.94 pp. | 8.82% |
| 5. Pretraining Plays an Integral Role in Improving Continual Learning Performance | 5 | ≈ 1.96 pp. | 8.91% |
| 6. VLAs Retain Knowledge that is Seemingly Forgotten | 7 | ≈ 1.67 pp. | 7.59% |
| 7. Conclusion and Discussion | 9 | ≈ 0.65 pp. | 2.95% |
| Acknowledgment | 10 | ≈ 0.17 pp. | 0.77% |
| Impact Statement | 10 | ≈ 0.47 pp. | 2.14% |
| References | 10 | ≈ 3.4 pp. | 15.45% |
| Appendix: Table of Contents | 14 | ≈ 1 pp. | 4.55% |
| A. More Continual Learning Results | 15 | ≈ 2.64 pp. | 12% |
| B. Experimental Setup Details | 17 | ≈ 2.41 pp. | 10.95% |
| C. Study on Other Factors that Contribute to VLA’s Continual Learning Behavior | 20 | ≈ 0.47 pp. | 2.14% |
| D. Per-Task Knowledge Transfer Curves: Pi0 vs. BC-Transformer | 20 | ≈ 2.39 pp. | 10.86% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Related Work; 3 Preliminaries
Introduces continual-policy learning and separates success from forgetting metrics.
- 4 Resistance to Forgetting; 5 Role of Pretraining
Tests replay size, algorithm choice and matched-architecture pretraining variants.
- 6 Knowledge Retention; 7 Conclusion
Uses component swaps and relearning to investigate apparently lost capabilities before scope limits.
Problem, claim and contribution
The broad resistance-to-forgetting title is made conditional in the abstract and experiments on experience replay; sequential training and EWC do not inherit the same finding.
Abstract, p.1; Table 2, p.5 · Main paper
Argument and section order
The analysis separates low forgetting from successful new-task learning before turning to the more mechanistic swap and recovery probes.
Sections 5-6, pp.5-8 · Main paper
Figures, captions and visual explanation
The opening heatmap grid makes task/checkpoint evolution visible across four suites; the later three-part diagram explicitly contrasts normal training, component swaps and relearning.
Figure 1, p.1; Figure 6, p.8 · Main paper
Tables, comparisons and evidence
The recovery result counts optimization steps while reusing the full original-task dataset, and the real-robot pilot is explicitly only three tasks in a single run.
Real-World Evaluation, p.5; recovery protocol and limitations, p.9 · Main paper
Appendix and reproducibility
Normalized forgetting exposes how a lower initial success rate can misleadingly improve absolute NBT; additional model-size and loss controls are labeled initial investigations.
Appendix A.2, p.16; Appendix C, p.20 · Main paper
Clarity and writing conventions
Knowledge retention is operationalized through faster relearning, while the discussion acknowledges benchmark concentration and replay retention costs.
Section 6, pp.8-9 · Main paper
What to reuse—and what not to infer
Transferable lesson: Pair retention metrics with acquisition quality, and distinguish rapid relearning in steps from learning with fewer examples.
Boundary: Replay remains important; component swaps and recovery are behavioral probes rather than direct proof of preserved representations, and most evidence shares LIBERO’s setting.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-07
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
- Appendix / supplement
- Appendix organization and main-text pointers were checked; focused text inspection of physical pages 16, 20. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
- Visually inspected PDF pages
- 1, 8
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.