Survey Paper on Machine Learning topic Curriculum Learning - 09/05/2026 22:21 EDT
Budget: ₹12,500 – ₹37,500 INR
**Project Title**
Research Assistant for Machine Learning Survey Paper on Curriculum Learning
**Project Overview**
I am working on a survey paper in machine learning focused on **curriculum learning in the modern deep learning era**. The paper is not a generic overview. It is intended to be a **methodologically sharp survey** that explains how curriculum learning has evolved, especially in the post-2020 period, across methods, domains, benchmarks, and evidence quality.
The survey uses:
* **2009–2020** as the foundational period
* **2021–2026** as the primary review window
The paper is organized around:
* a **two-track historical view**: pre-2020 classical stream and post-2020 scaling-era stream
* a **structural taxonomy** based on what the curriculum acts on during training
* an **evidence-centered review framework** with a master coding table for papers
I am **not looking for someone to ghostwrite the full paper**. I will be actively involved and writing alongside this process. I am looking for a strong research-oriented collaborator who can help me accelerate the literature review, paper structuring, evidence cataloging, and survey-building process.
**What I Need Help With**
I am looking for support in areas such as:
* identifying and organizing relevant papers in curriculum learning
* helping build and clean a **master paper-coding table**
* extracting structured information from papers, such as:
* curriculum unit
* difficulty signal
* scheduler / pacing
* controller / supervision source
* intended benefit
* domain
* evidence quality
* baselines used
* compute overhead
* reproducibility
* key contribution
* limitations
* helping distinguish **core curriculum-learning papers** from adjacent work such as benchmark papers, signal papers, or negative-result papers
* helping refine the structure, taxonomy, and section flow of the survey
* supporting synthesis across subdomains such as:
* reinforcement learning
* graph machine learning
* federated / distributed learning
* contrastive / self-supervised learning
* token / sequence / prefix-level curricula
* LLM pretraining and reasoning
* helping convert notes and rough structure into clearer survey sections
* optionally helping with figures, taxonomy diagrams, and clean academic presentation
**Ideal Background**
I am looking for someone with most or all of the following:
* strong familiarity with machine learning literature
* experience reading and synthesizing research papers
* comfort with survey papers or systematic literature reviews
* ability to organize technical information into structured comparison tables
* strong understanding of modern ML areas such as RL, SSL, LLMs, graph ML, or federated learning
* academic writing maturity and ability to think critically about evidence quality
* strong attention to detail and ability to separate hype from actual empirical support
**Nice to Have**
* prior experience assisting on a survey paper
* familiarity with Overleaf / LaTeX
* ability to help build clean taxonomy tables or visual diagrams
* comfort working with citation organization and structured literature matrices
**Deliverables**
Depending on fit, the work may include:
* a cleaned and expanded literature spreadsheet / coding table
* categorized reading list of core and adjacent papers
* structured paper notes and comparison summaries
* proposed taxonomy refinements
* section-wise synthesis notes
* support on turning rough notes into sharper survey structure
**Important Note**
This is a collaborative research support role, not a request to fully write the paper for me. I will be actively involved in the writing and direction. I am looking for someone who can help me think, structure, organize, and synthesize at a high level.
**To Apply, Please Share**
1. A short note about your background in machine learning / research literature
2. Any experience with survey papers, literature reviews, or technical synthesis
3. A relevant sample of prior work if available
4. Which of the following areas you are strongest in:
* RL
* LLMs
* SSL / contrastive learning
* graph ML
* federated learning
* general literature review / research synthesis
5. Your expected rate and availability over the next 1–2 weeks
**Screening Question**
Please briefly explain how you would differentiate:
* classical curriculum learning,
* self-paced learning,
* and training-dynamics-based curriculum methods
That will help me understand how you think about the area.
Research Assistant for Machine Learning Survey Paper on Curriculum Learning
**Project Overview**
I am working on a survey paper in machine learning focused on **curriculum learning in the modern deep learning era**. The paper is not a generic overview. It is intended to be a **methodologically sharp survey** that explains how curriculum learning has evolved, especially in the post-2020 period, across methods, domains, benchmarks, and evidence quality.
The survey uses:
* **2009–2020** as the foundational period
* **2021–2026** as the primary review window
The paper is organized around:
* a **two-track historical view**: pre-2020 classical stream and post-2020 scaling-era stream
* a **structural taxonomy** based on what the curriculum acts on during training
* an **evidence-centered review framework** with a master coding table for papers
I am **not looking for someone to ghostwrite the full paper**. I will be actively involved and writing alongside this process. I am looking for a strong research-oriented collaborator who can help me accelerate the literature review, paper structuring, evidence cataloging, and survey-building process.
**What I Need Help With**
I am looking for support in areas such as:
* identifying and organizing relevant papers in curriculum learning
* helping build and clean a **master paper-coding table**
* extracting structured information from papers, such as:
* curriculum unit
* difficulty signal
* scheduler / pacing
* controller / supervision source
* intended benefit
* domain
* evidence quality
* baselines used
* compute overhead
* reproducibility
* key contribution
* limitations
* helping distinguish **core curriculum-learning papers** from adjacent work such as benchmark papers, signal papers, or negative-result papers
* helping refine the structure, taxonomy, and section flow of the survey
* supporting synthesis across subdomains such as:
* reinforcement learning
* graph machine learning
* federated / distributed learning
* contrastive / self-supervised learning
* token / sequence / prefix-level curricula
* LLM pretraining and reasoning
* helping convert notes and rough structure into clearer survey sections
* optionally helping with figures, taxonomy diagrams, and clean academic presentation
**Ideal Background**
I am looking for someone with most or all of the following:
* strong familiarity with machine learning literature
* experience reading and synthesizing research papers
* comfort with survey papers or systematic literature reviews
* ability to organize technical information into structured comparison tables
* strong understanding of modern ML areas such as RL, SSL, LLMs, graph ML, or federated learning
* academic writing maturity and ability to think critically about evidence quality
* strong attention to detail and ability to separate hype from actual empirical support
**Nice to Have**
* prior experience assisting on a survey paper
* familiarity with Overleaf / LaTeX
* ability to help build clean taxonomy tables or visual diagrams
* comfort working with citation organization and structured literature matrices
**Deliverables**
Depending on fit, the work may include:
* a cleaned and expanded literature spreadsheet / coding table
* categorized reading list of core and adjacent papers
* structured paper notes and comparison summaries
* proposed taxonomy refinements
* section-wise synthesis notes
* support on turning rough notes into sharper survey structure
**Important Note**
This is a collaborative research support role, not a request to fully write the paper for me. I will be actively involved in the writing and direction. I am looking for someone who can help me think, structure, organize, and synthesize at a high level.
**To Apply, Please Share**
1. A short note about your background in machine learning / research literature
2. Any experience with survey papers, literature reviews, or technical synthesis
3. A relevant sample of prior work if available
4. Which of the following areas you are strongest in:
* RL
* LLMs
* SSL / contrastive learning
* graph ML
* federated learning
* general literature review / research synthesis
5. Your expected rate and availability over the next 1–2 weeks
**Screening Question**
Please briefly explain how you would differentiate:
* classical curriculum learning,
* self-paced learning,
* and training-dynamics-based curriculum methods
That will help me understand how you think about the area.