PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (41)

[1]
Prompt Curriculum Learning for Efficient LLM Post-Training
2025Zhaolin Gao, Joongwon Kim et al.
[2]
Agentic Reinforced Policy Optimization
2025Guanting Dong, Hangyu Mao et al.
[3]
Group Sequence Policy Optimization
2025Chujie Zheng, Shixuan Liu et al.
[4]
SPEED-RL: Faster Training of Reasoning Models via Online Curriculum Learning
2025Ruiqi Zhang, Daman Arora et al.
[5]
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
2025Shubham Parashar, Shurui Gui et al.
[6]
Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts
2025Haizhong Zheng, Yang Zhou et al.
[7]
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
2025Zafir Stojanovski, Oliver Stanley et al.
[8]
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
2025Yang Chen, Zhuoling Yang et al.
[9]
Self-Evolving Curriculum for LLM Reasoning
2025Xiaoyin Chen, Jiarui Lu et al.
[10]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[11]
DUMP: Automated Distribution-Level Curriculum Learning for RL-based LLM Post-training
2025Zhenting Wang, Guofeng Cui et al.
[12]
VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
2025Yu Yue, Yufeng Yuan et al.
[13]
Understanding R1-Zero-Like Training: A Critical Perspective
2025Zi-Yan Liu, Changyu Chen et al.
[14]
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
2025Qiying Yu, Zheng Zhang et al.
[15]
Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond
2025Liang Wen, Yunke Cai et al.
[16]
DISC: Dynamic Decomposition Improves LLM Inference Scaling
2025Jonathan Light, Wei Cheng et al.
[17]
DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
2025DeepSeek-AI, Daya Guo et al.
[18]
SFS: Smarter Code Space Search improves LLM Inference Scaling
2025Jonathan Light, Yue Wu et al.
[19]
Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play
2024Ziyu Ye, Rishabh Agarwal et al.
[20]
Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning
2024Amrith Rajagopal Setlur, Chirag Nagpal et al.

Showing 20 of 41 references

Founder's Pitch

"ACTOR-CURATOR is an automated curriculum learning framework enhancing reinforcement learning post-training efficiency and stability for large language models."

Reinforcement LearningScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

3/4 signals

7.5

Series A Potential

2/4 signals

5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/24/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.