Research & academia
Automating eval design and hillclimbing with Claude
Anthropic publishes an evaluation and iterative-improvement workflow.
Why it matters: Evaluation-driven iteration needs representative tasks and checks against overfitting.
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· Past edition · 11 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Research & academia
Anthropic publishes an evaluation and iterative-improvement workflow.
Why it matters: Evaluation-driven iteration needs representative tasks and checks against overfitting.
Research & academia
A controlled distillation study finds the effect of student-generated rollouts depends on the objective, learning rate and evaluation setting.
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Why it matters: Distillation comparisons need to separate rollout policy from objectives and learning rates.
Research & academia
Introduces an interactive world model that combines control, compressed memory and a distillation method for longer rollouts.
Why it matters: Interactive world models need both responsive controls and memory over long runs.
Research & academia
Tests emergent misalignment after narrow multimodal fine-tuning across commercial and open models, with partial mitigation results.
Why it matters: The reported experiments extend fine-tuning misalignment concerns to visual tasks.
Research & academia
Studies whether forged chat markers gain authority from reserved-token representations and tests the implications for prompt injection.
Why it matters: The encoding of a forged marker can affect how much authority a model gives untrusted text.
Research & academia
Examines whether additional reinforcement learning can undo defenses intended to impede distillation of model capabilities.
Why it matters: Post-distillation training can change whether an evaluated defense still holds.