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5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
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exploration

topic2 events
papersTODAY 04:00 UTC

On-Policy Self-Distillation Method Aims to Prevent Entropy Collapse in RL-Trained LLMs

A new arXiv paper proposes an approach called on-policy self-distillation that acts as a "policy reheater" for reinforcement learning with verifiable rewards. The authors target entropy collapse, a failure mode where model policies become overly concentrated, cutting rollout diversity and weakening the learning signal. The method is presented as a way to keep exploration alive during RL training of large language models.

reinforcement-learning/llm-training/reasoning/arxiv
papersTODAY 04:00 UTC

Dream-RSI Paper Proposes Recursive Self-Improvement via Evolving Worlds

A new arXiv preprint introduces Dream-RSI, a method aimed at recursive self-improvement for autonomous AI agents. The approach centers on exploration, using evolving environments to help agents find high-value solutions in complex domains. The work appears to target the difficulty of managing and improving exploration as agent capabilities grow.

agents/self-improvement/exploration/arxiv