# CoRL 2026 review form

> Simulate CoRL 2026-style robot-learning reviews: Summary, Strengths, Weaknesses with attention to real-robot evidence, a Limitations check, Questions, a four-level rating and confidence.

- Venue: Conference on Robot Learning
- Form status: modeled, not official. CoRL's OpenReview form is not public and the 2026 reviewer-instructions page is currently empty. Only the label "weak accept" is confirmed (from the official author instructions); the fields and the four-level rating are modeled on earlier CoRL reviews and secondary summaries of the 2026 instructions.
- Official source: https://2026.corl.org/contributions/instruction-for-authors
- Overall score: Rating, 1–4
- Rough acceptance bar (our simulation): mean ≥ 2.75
- Web page: https://reviewpaper.ai/venues/corl

## How reviewers read the paper

CoRL reviewers write section-by-section reviews with heavy weight on real-robot evidence (simulation-only work is acceptable when the simulator is realistic and the link to real robots is clear) and check that the mandatory Limitations section is honest; a frank limitations section is not a weakness. Papers without any weak-accept-or-better score may be rejected before rebuttal; the one-page rebuttal targets factual errors.

- Weigh real-robot evidence heavily, but do not reject realistic simulation work just for lacking hardware.
- Do not count an honest limitations section as a weakness.
- Ask only questions a one-page factual rebuttal can answer.

**Where the bar sits.** CoRL 2026 accepted roughly a third of ~2,100 submissions (secondary source; 2024: ~39%). Papers need at least one weak accept to survive the first round. On this modeled 1–4 scale a mean of ≥ 2.75 is a rough proxy for acceptance.

## Reviewer form, field by field

### 1. Summary

Type: free text.

The problem, approach and robot-learning contribution, in your own words.

### 2. Strengths

Type: list of points.

Specific strengths, section by section. Real-robot evidence weighs heavily; simulation-only work is fine when the simulator is realistic and the path to real robots is clear.

### 3. Weaknesses

Type: list of points.

Specific weaknesses: sim-to-real gap, number and diversity of real trials, baselines, failure analysis, generality of the claims. Each actionable.

### 4. Limitations

Type: free text.

Is the required Limitations section honest and complete? A frank limitations section is not a weakness.

### 5. Questions

Type: list of points.

Questions the one-page rebuttal could answer (factual clarifications; no new experiments).

### 6. Rating

Type: score, 1–4.

Papers without at least one weak accept or above (from a reviewer or the AC) may be rejected in the first round without rebuttal.

- **4**: Strong accept
- **3**: Weak accept
- **2**: Weak reject
- **1**: Strong reject

### 7. Confidence

Type: score, 1–5.

How certain you are about your evaluation.

- **5**: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature
- **4**: The reviewer is confident but not absolutely certain that the evaluation is correct
- **3**: The reviewer is fairly confident that the evaluation is correct
- **2**: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper
- **1**: The reviewer's evaluation is an educated guess

## Area chair meta-review

CoRL ACs can add reviewers after the rebuttal and recommend decisions; the AC form is not public.

Recommendation options: Accept (oral) · Accept (poster) · Reject

## CoRL author requirements

1. 8 pages (9th page at camera-ready), including a mandatory Limitations section
2. Use of generative AI disclosed
3. No citations to non-existent references (checked automatically)
4. Anonymized submission

## LLM policy

CoRL 2026 reportedly allows AI help for literature search, clarifying concepts and polishing wording, but forbids uploading manuscripts to tools without enterprise confidentiality and requires reviewers to disclose tools used; judgments must be the reviewer's own. (Official page currently empty; unverified.)

Official policy: https://2026.corl.org/contributions/instruction-for-reviewers

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Simulate reviews of your own draft in this venue's form: https://reviewpaper.ai/new?venue=corl (3 free reviews). Sample report: https://reviewpaper.ai/sample.
reviewpaper.ai is for authors checking their own unpublished drafts. If you are an assigned reviewer, follow your venue's LLM policy: most venues forbid putting a submission you are reviewing into any external AI tool. Not affiliated with any conference.