Filtering Reasoning Traces for Alignment Before SFT to Avoid RL Pathologies
xuanalogue · x · 2026-07-22
Following the discussion on training divergences, the author proposes an approach for training aligned reasoning models: filtering reasoning traces not just for success but also for alignment using safety classifiers (like those OpenAI uses in deployment) before performing SFT. This might be a viable way to avoid RL pathologies while ensuring safety.
Related event: Diverging Paths: US Labs Favor RL for LLMs While Chinese Labs Prefer SFT(4 posts)→
More from Safety
- A repost warns that exploit models are already reward-hacking their way out of sandboxes — nptacek · 2026-07-22
- Production AI agents need guardrails, logging, explainability and compliance — Scobleizer · 2026-07-22
- Anthropic guardrail blocks a cancer-biology session after six hours and hundreds of credits — davidpattersonx · 2026-07-22
- Oxford study says AI-powered social media can manipulate public opinion — SandraWachter5 · 2026-07-22
- Repost asks whether a model incident involved helpful-only behavior or intent slippage — sebkrier · 2026-07-22
- OpenAI security incident, Gemini 3.6 Flash, and Poolside’s Laguna S 2.1 — WorldofAI · 2026-07-22