Hunyuan-A13B Technical Report

arXiv:2609.27284 · cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/Penguin-Scrolls/PenguinScrolls

Terminology

• Benchmark — 45× in this paper · explained in 2 episodes
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Thinking mode — 5× in this paper · explained in 2 episodes
• Verifier — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Hallucination — 3× in this paper · explained in 3 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• KV cache — 2× in this paper · explained in 3 episodes
• Question Answering — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• American Invitational Mathematics Examination — 1× in this paper
• Artificial General Intelligence — 1× in this paper
• BIG-Bench Hard — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Retrieval-Augmented Generation — 1× in this paper
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 16 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Standardized Benchmarks — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Program Synthesis with Large Language Models
• LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
• MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
• McEval: Massively Multilingual Code Evaluation
• Evaluating Large Language Models Trained on Code
• Training Verifiers to Solve Math Word Problems
• DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
• SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
• DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
• Are We Done with MMLU?
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
• Measuring Mathematical Problem Solving With the MATH Dataset
• RULER: What's the Real Context Size of Your Long-Context Language Models?
• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
• Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
• ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
• Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
• Generative Reward Models
• SysBench: Can Large Language Models Follow System Messages?

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• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection