Aman Sanger (@amanrsanger)
Kimi k2.5를 여러 베이스 모델과 perplexity 기반 평가로 비교한 결과, 가장 강력한 모델로 평가했다고 언급했습니다. 이어서 continued pre-training과 고비용 RL을 4배 규모로 확장해 성능을 끌어올렸다고 밝혀, 최신 모델 평가와 학습 전략 측면에서 중요한 내용입니다.
https://x.com/amanrsanger/status/2035079293257359663
#kimi #llm #reinforcementlearning #pretraining #evaluations

Aman Sanger (@amanrsanger) on X
We've evaluated a lot of base models on perplexity-based evals and Kimi k2.5 proved to be the strongest!
After that, we do continued pre-training and high-compute RL (a 4x scale-up).
The combination of the strong base, CPT and RL, and Fireworks' inference and RL samplers make
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07 Paramétrer une évaluation par compétence dans Pronote
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