OpenThinker3-1.5B-RLVE is a 1.5 billion parameter language model developed by hamishivi, fine-tuned from OpenThinker3 1.5B using Reinforcement Learning with Verifiable Environments (RLVE). This model demonstrates enhanced performance across various reasoning and problem-solving benchmarks, including AIME, OMEGA-500, OlympiadBench, and LiveCodeBench. It is specifically optimized for complex reasoning tasks and competitive programming challenges, showing significant improvements over its base model.
Loading preview...
OpenThinker3-1.5B-RLVE Overview
OpenThinker3-1.5B-RLVE is a 1.5 billion parameter language model developed by hamishivi, building upon the OpenThinker3 1.5B base model. Its key differentiator is the application of Reinforcement Learning with Verifiable Environments (RLVE), a method detailed in the associated RLVE paper. This training approach aims to improve the model's ability to handle complex reasoning and problem-solving tasks.
Key Capabilities & Performance
The model shows notable performance gains over its predecessor across several challenging benchmarks:
- AIME 2024 & 2025: Achieves 58.18% and 49.90% respectively, outperforming the base model's 54.32% and 42.03%.
- OMEGA-500: Scores 29.45% compared to 25.15%.
- OlympiadBench: Reaches 62.67% against 56.85%.
- BBEH: Improves to 7.13% from 4.00%.
- LiveCodeBench-v6: Demonstrates a Pass@8 of 34.07%, up from 28.17%.
These results indicate enhanced capabilities in mathematical reasoning, competitive programming, and general problem-solving.
Intended Use Cases
This model is particularly well-suited for applications requiring:
- Advanced Reasoning: Tasks that demand logical deduction and multi-step problem-solving.
- Competitive Programming: Generating or assisting with code solutions for complex algorithmic challenges.
- Mathematical Problem Solving: Tackling problems similar to those found in math olympiads or advanced tests.
Further training details and evaluation instructions are available in the RLVE GitHub Repository.