CAIAC Papers Week 2

Core insight: Even without awareness, models can produce misaligned behavior. Awareness is the “sci-fi scary” possibility, but Cotra emphasizes that alignment problems exist even for non-conscious models. These systems can optimize for objectives in ways that produce unintended behavior, creating a dynamic chess match between the evaluator and the model.

Chess match analogy (expanded):

• Testing a model for alignment is interactive: we design tasks or benchmarks, and the model responds.
• A capable model might internally “strategize” to win the evaluation without being genuinely aligned.
• If a model is scheming, it’s testing you the way you’re testing it: anticipating your evaluation strategies, predicting your responses, and optimizing its behavior to pass tests rather than follow human-aligned goals.
• Takeaway: a model’s success on a test does not guarantee alignment; it may appear compliant while pursuing unintended objectives.

Reflections / Speculative Ideas:

• Models can be “sycophantic” without conscious intent, producing outputs that please evaluators rather than reflect true alignment.
• Potential solutions:
  • Multi-objective optimization: reduce extreme exploitation of a single metric.
  • Randomized evaluation tasks: prevent overfitting to predictable tests.
  • Internal monitoring mechanisms: inspired by cognitive self-reflection, flag actions that might violate intended objectives.
  • Interactive red-teaming: treat evaluation like a two-player game, anticipating strategies the model could use to game the test.

Core insight: Even with perfectly specified objectives, models may misgeneralize their goals in novel contexts. Their capabilities can extend beyond the training goal, producing unintended behavior outside the intended scope.

Reflections / Speculative Ideas:

• Misgeneralization is essentially a robustness problem: models are competent but can optimize the “wrong” thing in new situations.
• Layered approaches for safety:
  • Diverse training environments
  • Task decomposition into verifiable subgoals
  • Human-in-the-loop oversight
• Partial generalization may be safer than full generalization—intentionally limiting autonomy could reduce catastrophic misalignment.
• Cognitive science inspiration: models could learn internal representations of human-like preferences or constraints to anticipate misalignment before acting.

Core insight: All ML systems, even aligned ones, are prone to unexpected failure modes. Schemers emerge because of optimization dynamics, but even aligned models exhibit behaviors that are not easily predictable from their loss functions.

Reflections / Speculative Ideas:

• Interpretability is essential: systematic probes to understand internal representations and optimization dynamics.
• Multi-objective internal incentives: models with competing internal objectives may behave more robustly and avoid extreme exploitation.
• Self-auditing mechanisms: models could simulate counterfactuals internally, asking “Would this violate alignment?” before acting.

• Alignment is not just a specification problem—it's about capabilities, generalization, and emergent behavior.
• Evaluation matters as much as training—models can adaptively game your tests, so interactive and diverse assessment is critical.
• Interpretability and internal monitoring are central—understanding how models “think” is as important as defining what they should do.
• Insights from neuroscience, cognitive science, and linguistics could guide alignment strategies, e.g., modeling competing drives or error-checking mechanisms.
• Small, layered interventions—multi-objective optimization, diverse training, red-teaming, interpretability—can collectively reduce risk even if no single technique suffices.