Mathematical Reasoning
The reasoning capability that large models exhibit in solving mathematical problems, covering arithmetic, algebra, geometry and related areas. It is one of the core task domains for evaluating the reasoning ability of large models.
Timeline (last 90 days)
KAPPO's experiments cover two task types, mathematical reasoning and code generation, and the results show that it provides direct control over the degree of sharpening.
After supervised fine-tuning (SFT), the gains brought by UpLoop can also extend to mathematical reasoning tasks.
Experiments on Qwen3 models for mathematical reasoning tasks show that MTA-OPSD consistently outperforms existing OPSD methods, with an average gain of up to +5.9 relative to the base model.
Mathematical reasoning is a typical scenario for long-sequence distillation because solving a problem often requires dozens of derivation steps, and the student easily deviates from the teacher's path midway.
Mathematical problems often have clear right-or-wrong standards, making them ideal benchmarks for measuring model capability.
Benchmark scores alone make it difficult to determine whether a model has truly mastered mathematical reasoning; step-by-step verification of the problem-solving process by domain experts is required.
All Facts (6)
KAPPO's experiments cover two task types, mathematical reasoning and code generation, and the results show that it provides direct control over the degree of sharpening.
50%UnverifiedAfter supervised fine-tuning (SFT), the gains brought by UpLoop can also extend to mathematical reasoning tasks.
50%UnverifiedExperiments on Qwen3 models for mathematical reasoning tasks show that MTA-OPSD consistently outperforms existing OPSD methods, with an average gain of up to +5.9 relative to the base model.
50%UnverifiedMathematical reasoning is a typical scenario for long-sequence distillation because solving a problem often requires dozens of derivation steps, and the student easily deviates from the teacher's path midway.
50%UnverifiedMathematical problems often have clear right-or-wrong standards, making them ideal benchmarks for measuring model capability.
50%UnverifiedBenchmark scores alone make it difficult to determine whether a model has truly mastered mathematical reasoning; step-by-step verification of the problem-solving process by domain experts is required.
50%