#supervised learning
473 related articles
·3 min
Gemini's Hilarious Reply Goes Viral on Reddit: A Deep Dive into the AI Personification Trend
Google Gemini went viral on Reddit for a humorous reply, dubbed an "undercover wasp." This article explores the technical origins of AI humor, RLHF-driven personality shaping, and the future of AI personification.
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·2 min
How AI Technology Is Reshaping Cognitive Research on Wild Primates
Explore how AI is breaking through bottlenecks in wild primate cognitive research. From facial recognition and behavior classification to sound analysis, AI reveals secrets of primate memory, social cognition, and communication.
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·3 min
Six Months and 124 Iterations: Conquering Reactive Play in Breakout with PPO
An RL enthusiast spent 6 months and 124 iterations to achieve reactive play in Atari Breakout using PPO. A deep dive into PPO tuning challenges and real-world RL engineering.
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·2 min
Google Releases Gemini 3.6 Flash and Two Other New Models: Comprehensive Expansion of the Flash Family
Google launches Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber, further expanding its lightweight AI product line. Analysis of positioning, differentiation strategy, and developer impact.
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·3 min
Evaluating Open-Source AI Mathematical Reasoning: Latest Progress and Core Challenges
In-depth analysis of open-source AI models' latest progress in mathematical reasoning, exploring evaluation challenges like data contamination and benchmark saturation, and how formal verification and chain-of-thought methods drive more objective assessment.
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·3 min
LLM Post-Training on an 8GB GPU: A Practical Guide to SFT, DPO, and GRPO
Learn how to complete LLM post-training on a consumer GPU with just 8GB VRAM, covering SFT, DPO, and GRPO methods using LoRA quantization and other techniques.
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·3 min
Explorative Modeling: How Best-of-K Training Strategies Improve Model Learning Efficiency
Explorative modeling lets models generate K candidate predictions and learn from the best one, introducing exploration into training. This article analyzes Best-of-K training strategy principles, applications, and challenges.
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·3 min
H-JEPA-LM: A Non-Generative Language Model That Predicts in Latent Space
Deep dive into H-JEPA-LM, a non-autoregressive language model that predicts in latent space using hierarchical abstraction and world-model-style planning, challenging mainstream LLM paradigms.
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·3 min
The Fundamental Conflict Between Backpropagation and Continual Learning: Can Catastrophic Forgetting Ever Be Truly Solved?
Exploring the fundamental conflict between backpropagation and continual learning, analyzing the roots of catastrophic forgetting, limitations of current solutions, and whether local learning or neuromorphic computing can offer true breakthroughs.
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·3 min
Voice-Pro: An Open-Source AI Voice Tool Integrating TTS, Voice Cloning, and Speech Recognition
Voice-Pro is a trending open-source AI voice tool on GitHub integrating Edge-TTS, F5-TTS, CosyVoice zero-shot voice cloning, Whisper speech recognition, and more via a Gradio interface for TTS, cross-language dubbing workflows.
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·6 min
Using RL to Please the Reward Model: The Reward Hacking Concern Behind Soaring Elo Scores
When RL continuously optimizes models to please reward models, do soaring Elo scores truly represent capability gains? A deep dive into Reward Hacking in RLHF, Goodhart's Law in AI, and industry countermeasures.
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·4 min
Heretic Uncensoring Technology Explained: Practical Applications on Jamba2 and Qwen3.5 Open-Source Models
Deep dive into Heretic uncensoring technology applied to Jamba2-Mini, Qwen3.5-9B, and 27B open-source models, exploring how refusal rates dropped from 97% to 4% and the safety debates involved.
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·5 min
The Complete Learning Path to Understanding the Kimi K3 Technical Report
A systematic learning path for understanding the Kimi K3 technical report, covering MoE, MLA, distributed training, and modern post-training techniques.
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·4 min
Automatic Tibetan-Chinese Bilingual Subtitle Generation: A Complete Technical Breakdown
A complete technical guide to automatic Tibetan-Chinese bilingual subtitle generation, covering Tibetan ASR (Whisper/wav2vec), machine translation (NLLB), timeline alignment, and subtitle export for low-resource language creators.
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·3 min
Three Words to Break Claude Opus 5? Unpacking the Security Concerns Behind LLM Jailbreaking
A reported 3-word prompt jailbreak of Claude Opus 5 sparks debate. We analyze the technical nature of LLM jailbreaks, alignment fragility, and defense-in-depth strategies for enterprise AI security.
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·3 min
Three Words to Break Claude Opus 5? Unpacking the Security Concerns Behind LLM Jailbreaking
A reported 3-word jailbreak of Claude Opus 5 sparks debate. We analyze LLM jailbreak mechanics, alignment fragility, and defense-in-depth strategies for AI security.
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·2 min
Distilling DeepSeek to GPT-OSS: Why Censorship Doesn't Transfer
When distilling DeepSeek to GPT-OSS, censorship mechanisms don't transfer. This article analyzes the separability of capabilities vs. behavioral constraints in knowledge distillation and its implications for AI safety.
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·2 min
Distilling DeepSeek to GPT-OSS: Why Censorship Doesn't Transfer
When distilling DeepSeek to GPT-OSS, model censorship doesn't transfer. This article analyzes the separability of capabilities and behavioral constraints in knowledge distillation and its implications for AI safety.
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·4 min
More Imitation Learning Data Leading to Worse Results? A Deep Dive into Compounding Errors and Data Quality Traps
Through a real game AI navigation case, this article deeply analyzes why more data can worsen imitation learning, covering compounding errors, distribution shift, data quality issues, and DAgger solutions.
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·4 min
More Imitation Learning Data Makes Things Worse? A Deep Dive into Compounding Errors and Data Quality Traps
Through a real game AI navigation case, we deeply analyze why more data can worsen imitation learning, covering compounding errors, distribution shift, data quality issues, and DAgger solutions.
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