LLM reasoning

2 items

RESEARCHDEV.to AI·4/13/2026

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive EffectiveReinforcement Learning for LLM Reasoning

This content explores a novel approach to improve Reinforcement Learning for Large Language Model (LLM) reasoning by focusing on "high-entropy minority tokens". It proposes that these less frequent yet highly informative tokens are key drivers for effective learning, challenging the conventional 80/20 rule.

Token Analysis reinforcement learning Natural Language Processing LLM reasoning

RESEARCHDEV.to AI·9d ago

I read a multi-agent reasoning paper, built the Claude-native version, and measured everything

A paper highlights the superiority of AI agents that share internal reasoning states, leading to an 8.3-point average accuracy gain. The author built a Claude-native version using Anthropic's extended thinking API, adapting the internal state sharing concept to thinking-block relay, and discusses implementation challenges.

Claude API multi-agent systems LLM reasoning AI agents