Analysis
A team showcased an impressive approach to the LLM-jp FT-LLM competition, achieving a commendable 8th place by focusing on advanced techniques. Their success highlights the power of combining innovative methods such as context window expansion with synthetic data generation and reinforcement learning for improved performance in complex mathematical reasoning tasks.
Key Takeaways
- •The team used YaRN for context window expansion, allowing for longer Chain of Thought reasoning.
- •They constructed synthetic reasoning data using gpt-oss-120b, including both Chain of Thought and Tool-Integrated Reasoning.
- •A multi-agent parallel inference pipeline, combined with a majority vote, was used to generate 160 answer candidates for each question.
Reference / Citation
View Original"The team "Tengentoppa" achieved 8th place (tied, 22 teams in total) with a correct answer rate of 61.6%."
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