Lei Cui

MisterClaw: Run PicoClaw AI on Your MiSTer FPGA youtu.be/CK4210q3k4Y?si… via @YouTube @MiSTerFPGA @SipeedIO #MisterClaw #PicoClaw #MisterFPGA #OpenClaw #Retrogaming

github.com/wolfshow/Miste… MisterClaw meme designed by Nano Banana @MiSTerFPGA @SipeedIO

MisterClaw: World's first @SipeedIO PicoClaw AI assistant on MiSTer FPGA (DE10-Nano) #MisterFPGA #OpenClaw #PicoClaw #RetroGaming #MisterClaw github.com/wolfshow/Miste…

Built a tiny AI assistant powered by @SipeedIO PicoClaw 🦞 on my MiSTer FPGA. 10MB RAM, 600+ arcade games, all managed via Telegram. Retro gaming meets AI. 🎮🤖 #MiSTerFPGA #OpenClaw #PicoClaw #RetroGaming

Just got the N64 x7, should I solder it myself? @krikzz

Black Friday sales begins! 20% off on all products at krikzz.com. RT this message and you will have a chance to get EverDrive or FXPAK for free! Winner will be chosen 02.12.2025 #GIVEAWAY

#AesCoder on Design Arena

BitDistill finetunes any full-precision LLMs into 1.58-bit for specific tasks with the same peformance

Thrilled to introduce #PART, a new method to protect LLM reasoning from unauthorized distillation while keeping it transparent for users. By removing self-talk and reordering conclusions, we disrupt illicit training and preserve valuable information. arxiv.org/abs/2510.11545

Beyond just text quality! We're introducing #DocReward, a model that evaluates and improves the visual structure and style of documents. In our tests, DOCREWARD achieved a 60.8% win rate in generating human-preferred documents, compared to GPT-5's 37.7%. arxiv.org/abs/2510.11391

#Kosmos-2.5 now in Hugging Face

How do you stop an LLM from getting “thrown off” by a few wild tokens? UCAS, CUHK, HKUST, and Microsoft Research researchers think they’ve cracked it with Geometric-Mean Policy Optimization (GMPO) — a twist on GRPO that tames outliers by optimizing the geometric mean of token-level rewards. Result: more stable training, +4.1% on math benchmarks, +1.4% on multimodal reasoning.

Top AI Papers of The Week (July 28 - August 3): - GEPA - Graph-R1 - AlphaEarth - Self-Evolving Agents - Hierarchical Reasoning Model - Efficient Attention Mechanisms - Geometric-Mean Policy Optimization Read on for more:

Microsoft Research introduces Geometric-Mean Policy Optimization (GMPO)! A new RL method that stabilizes LLM reasoning by maximizing the geometric mean of token-level rewards. No more unstable updates!

GMPO outperforms GRPO by 4.1% on math & 1.4% on multimodal reasoning benchmarks. It achieves better stability and performance, moving us closer to reliable AI. Learn more & get the code: Paper: huggingface.co/papers/2507.20… Code: github.com/callsys/GMPO

RL(LLM) - Pisałem ostatnio o GSPO. A dzisiaj publikacje na temat -> GMPO - Geometric-Mean Policy Optimization, ARPO - Agentic Reinforced Policy Optimization, IRL - Inverse RL … Chyba najbardziej kwitnący obszar treningowy LLM. U nas Bielik-v3 też już trenowany RL (GRPO, DR-GRPO, DAPO, GSPO - przygotowane) … czekamy na nową bazę. Wczoraj zakończyłem pracować nad największym polskim matematycznym datasetem treningowym RL - blisko 500k unikalnych i weryfikowalnych polskich zdań. Będzie moc 😁 Team - Krzysiek Ociepa @ChrisOciepa , Łukasz Flis, Adrian Gwoździej, Krzysiek Wróbel i moje wsparcie - pracuje teraz na pełnych obrotach. Dream team🤩Praca sama idzie. Nowe pomysły wdrażane w kilka minut, nie trzeba za wiele mówić - delivery najważniejsze. Ekstra pracuje się w takiej ekipie. Codziennie mamy postęp! Ogromne wsparcie @Cyfronet ❤️🔥

8. Geometric-Mean Policy Optimization 🔑 Keywords: Geometric-Mean Policy Optimization, Policy Updates, Token-Level Rewards, Multimodal Reasoning, AI Native 💡 Category: Natural Language Processing 🌟 Research Objective: - The research aims to stabilize policy updates in large language models through Geometric-Mean Policy Optimization (GMPO), enhancing the performance on mathematical and multimodal reasoning benchmarks. 🛠️ Research Methods: - GMPO introduces the use of geometric mean for token-level rewards to provide a less sensitive approach to outliers and maintain stable importance sampling ratios. Comprehensive theoretical and experimental analyses are conducted to validate GMPO's design and stability benefits. 💬 Research Conclusions: - GMPO demonstrates improved stability and a performance increase, surpassing GRPO by 4.1% on mathematical benchmarks and 1.4% on multimodal reasoning benchmarks like AIME24, AMC, MATH500, OlympiadBench, Minerva, and Geometry3K. 👉 Paper link: huggingface.co/papers/2507.20…

[CL] Geometric-Mean Policy Optimization Y Zhao, Y Liu, J Liu, J Chen... [Microsoft Research] (2025) arxiv.org/abs/2507.20673

Geometric-Mean Policy Optimization Using geometric mean for the importance ratio, similar to GSPO (arxiv.org/abs/2507.18071).

New paper: #GMPO beats GRPO by simply switching from arithmetic → geometric mean for token rewards! ✅ More stable training (no extreme importance sampling ratios) ✅ Better exploration (higher entropy throughout training) huggingface.co/papers/2507.20…