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 $10K - $14K 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 (25)

[1]
Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards
2025Xinyu Tang, Yu-Liang Zhan et al.
[2]
CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning
2025Shijie Zhang, Guohao Sun et al.
[3]
Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
2025Xumeng Wen, Zihan Liu et al.
[4]
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
2025Shubham Parashar, Shurui Gui et al.
[5]
The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
2025Xinyu Zhu, Mengzhou Xia et al.
[6]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[7]
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
2025Yang Yue, Zhiqin Chen et al.
[8]
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
2025Qiying Yu, Zheng Zhang et al.
[9]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[10]
Qwen2.5 Technical Report
2024Qwen An Yang, Baosong Yang et al.
[11]
Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
2024Bradley Brown, Jordan Juravsky et al.
[12]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[13]
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
2024Zhihong Shao, Peiyi Wang et al.
[14]
Let's Verify Step by Step
2023H. Lightman, Vineet Kosaraju et al.
[15]
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
2023Rafael Rafailov, Archit Sharma et al.
[16]
Training language models to follow instructions with human feedback
2022Long Ouyang, Jeff Wu et al.
[17]
Chain of Thought Prompting Elicits Reasoning in Large Language Models
2022Jason Wei, Xuezhi Wang et al.
[18]
Evaluating Large Language Models Trained on Code
2021Mark Chen, Jerry Tworek et al.
[19]
Measuring Mathematical Problem Solving With the MATH Dataset
2021Dan Hendrycks, Collin Burns et al.
[20]
Learning to summarize from human feedback
2020Nisan Stiennon, Long Ouyang et al.

Showing 20 of 25 references

Founder's Pitch

"Develop ACE, an adaptive error penalty system to correct overconfidence in RL-enhanced LLM reasoning."

LLM TrainingScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

2/4 signals

5

Series A Potential

0/4 signals

0

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.