Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning
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2512.15687
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18
Chmielewski
Eryk-Chmielewski
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✅ New Article: *Operating an SI-Core (v0.1)*
Title:
🛠️ Operating SI-Core: Dashboards, Playbooks, and Human Loops
🔗 https://huggingface.co/blog/kanaria007/operating-si-core
---
Summary:
Designing an SI-Core is only half the job — the other half is *running it safely at 03:00*.
This guide is a *non-normative ops runbook* for SRE/Ops teams and governance owners: what to put on the *one-page dashboard*, how to wire *alerts → actions*, when to use *safe-mode*, and how to answer the question that always arrives after an incident:
> “Why did the system do *that*?”
---
Why It Matters:
• Turns “auditable AI” into *operational reality* (not a slide deck)
• Makes *ethics + rollback* measurable, actionable, and drillable
• Clarifies how humans stay in the loop without becoming the bottleneck
• Provides templates for *postmortems, escalation, and regulator-grade explanations*
---
What’s Inside:
*Core Ops Dashboard (1 page):*
• Determinism/consistency, ethics/oversight, rollback/recovery, coverage/audit — with drill-downs that reach offending decisions in *two clicks*
*Alert → Runbook Patterns:*
• Examples for ethics index drops and rollback latency degradation
• Stabilization actions, scoped safe-mode, and governance handoffs
*Human-in-Loop Operations:*
• Safe-mode scopes (domain/tenant/region/risk)
• “Why?” view for any effectful action (structured explanation export)
*Reliability Muscle:*
• Incident templates, chaos drills, on-call handoffs, and capacity planning (because SI-Core accumulates structure over time)
---
📖 Structured Intelligence Engineering Series
A field manual for keeping structured intelligence upright — and explainable — under real-world pressure.
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