LLM optimization

Kuaishou Tech

Apr 24, 2025 · Artificial Intelligence

Two‑Stage History‑Resampling Policy Optimization (SRPO) for Large‑Scale LLM Reinforcement Learning

The article introduces SRPO, a two‑stage history‑resampling reinforcement‑learning framework that systematically tackles common GRPO training issues and achieves state‑of‑the‑art performance on both math and code benchmarks with far fewer training steps, while also revealing emergent self‑reflection behaviors in large language models.

LLM optimizationSRPOcross-domain training

0 likes · 12 min read

Two‑Stage History‑Resampling Policy Optimization (SRPO) for Large‑Scale LLM Reinforcement Learning

Tencent Cloud Developer

Jul 30, 2024 · Artificial Intelligence

A Systematic Guide to Prompt Engineering: From Zero to One

This guide walks readers from beginner to proficient Prompt Engineer by outlining the evolution of prompting, introducing a universal four‑component template, and detailing a five‑step workflow—including refinement, retrieval‑augmented generation, chain‑of‑thought reasoning, and advanced tuning techniques—plus evaluation metrics for LLM performance.

AI promptingChain-of-ThoughtLLM optimization

0 likes · 51 min read

A Systematic Guide to Prompt Engineering: From Zero to One

NetEase Cloud Music Tech Team

Dec 28, 2023 · Frontend Development

Lossless Design-Frontend Collaboration: The Evolution of NetEase Cloud Music's Design Collaboration Practice

Since 2021, NetEase Cloud Music’s Design Platform has evolved its design‑frontend workflow through three stages—engineering phase 1.0, phase 2.0, and the AI‑driven intelligent phase—by introducing the C2D2C (Code‑to‑Design‑to‑Code) methodology, unified design systems, LLM‑enhanced code, and generative AI tools, cutting communication overhead and boosting designer and developer productivity by up to 200 %.

AI designC2D2CD2C

0 likes · 31 min read

Lossless Design-Frontend Collaboration: The Evolution of NetEase Cloud Music's Design Collaboration Practice