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Awesome Cursor Training Tutorial: How Cursor Scales RL for AI-Powered Code Assistance
Zhan Shi, Rui Sun, Bing He
How Good Are GLM5.2 and Kimi-K3: Create Your Own Benchmark and Revisit Your Training Recipe
Bing He, Rui Sun, Zhan Shi, Hanqing Lu, Yisi Sang, Yuhao Zhang, Hao Zhou
The Hidden Language of AI: How Chat Templates Reveal the Evolution of LLMs
Zhan Shi, Rui Sun, Bing He (The Prompt Architects)
Designing Self-evolving Coding Agents for Terminal-Bench 2.0: A Systematic Exploration
Abstract. We present a systematic exploration of designing self-evolving coding agents for Terminal-Bench 2.0, a benchmark of 89 diverse command-line tasks. Starting from a vanilla ReAct agent powered by Claude Opus 4.6 (65.91%), we conduct large-scale controlled experiments across seven groups — establishing baselines, analyzing the performance ceiling (pass@5: 85.88%), exploring four harness components (prompt, memory, tools, skills), refining skill evolution strategies, developing self-verification mechanisms, and decomposing evolution into a multi-agent system (MAS-Evolver). Our investigation reveals that among all mutable components, skill evolution delivers the consistent improvement, and nudged self-verification mechanism further boosts the performance. The MAS-Evolver validates that separating analysis, authoring, and adversarial review into specialized agents produces quality-gated skills (+7.03pp over vanilla). Altogether, our best self-evolution algorithm achieves 76.47%, a +10.56pp improvement over the Opus 4.6 vanilla baseline, ranked top #7 in the terminal bench 2.0 leaderboard.