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Public story · 2026-02-26 · source-backed
First unified framework for stable agentic reinforcement learning. Decomposes policy gradient into four core design dimensions, proposes SAMPO achieving consistent training stability. If training LLM-based agents with RL (tool-use fine-tuning, reward modeling), this provides the first practical recipe for avoiding instability.
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Simon Willison released LLM / Shared entity: LLM / Shared topic / What happened next
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; overlapping topics (agent, agentic, design).
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; overlapping topics (agent, agentic).
LLM uses OpenAI / Shared entity: LLM / What happened next / Tension
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; picks up the LLM thread on 2026-07-27.
Simon Willison released LLM / Shared entity: LLM / What happened next / Tension
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-08-16.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-06-19.
Linked by a graph relationship (Simon Willison released LLM); both cover LLM; picks up the LLM thread on 2026-06-18.
LLM uses OpenAI / Shared entity: LLM / What happened next
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; picks up the LLM thread on 2026-07-31.
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; picks up the LLM thread on 2026-06-19.