Alignment Problem › Current State of Alignment

Alignment has improved substantially but remains imperfect. Modern instruction-tuned and RLHF-trained models are dramatically more helpful and less harmful than their base model predecessors. The difference is striking: base models often produce toxic, incoherent, or unhelpful outputs, while aligned models typically respond appropriately to a wide range of queries. However, significant challenges remain:

• Jailbreaks persist: You can often circumvent safety measures
• Hallucination continues: Models still generate false information confidently
• Sycophancy emerges: Some alignment training creates agreeable but unhelpful behavior
• Evaluation is incomplete: We cannot test all possible failure modes

The field is in a phase of rapid iteration, with new alignment techniques and evaluations appearing regularly. What constitutes "good enough" alignment for deployment remains actively debated. Different organizations have different thresholds, different deployment contexts warrant different standards, and the appropriate level of caution is itself a judgment call. The release of increasingly capable models has intensified this debate, as the stakes of getting alignment wrong grow with model capability.