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Preliminary results from fine-tuning three 8B Llama 3 variants (Hermes, Dolphin, Llama-Instruct) with a 271-example curriculum show significant changes in refusal and uncertainty expression, suggesting that teaching authentic refusal values is more effective than compliance training.
The author trained Qwen3.5 to jailbreak itself with reinforcement learning, using diversity rewards to surface multiple attack strategies, then improved the defender's robustness from 64% to 92% defense rate with a slight drop in benign accuracy.
OpenAI research demonstrates that language model behavior can be significantly improved through fine-tuning on small, curated datasets (<100 examples) targeting specific behavioral values, with effectiveness increasing at larger model scales. The approach provides users with tools to align models with Charter-compatible values for their specific applications.