Language Model Adaptation and Scaling
Defend adaptation and compute-allocation choices, explicitly limiting conclusions to the tested scale and domain.
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Defend adaptation and compute-allocation choices, explicitly limiting conclusions to the tested scale and domain.
Defend a probabilistic mixture fit using predictive checks rather than presenting a latent component as a proven real-world category.
Defend a low-rank reconstruction of a small matrix using singular values, measured error and a rank-selection rationale.
Defend a model and feature-set choice using held-out evidence, complexity, cost and the limitations of the selection procedure.
Explain a saddle point with a Hessian example and defend the gradient checks in a reusable calculus notebook.
Defend a precision and stopping policy using measured accuracy, memory and computation costs.
Present a tested feature-normalization module and defend its dtype, shape, copy and constant-column policies.
Construct and execute a nonconvex counterexample with different initializations, using its outcomes to delimit the convex convergence guarantee.
Compute and plot predictive distributions under two stated priors on the same data; justify the uncertainty change without interpreting likelihood as posterior probability.
Deliver a runnable training command, environment record and checkpoint recovery test that another person can execute.
Produce a 2-page critical review with contribution, strongest/weakest evidence, limitations, open questions and a falsifiable follow-up experiment.
Defend the evaluation and reward design, identifying a behavior that exploits the reward without solving the intended task.
Write a 6–10 page replication report that attributes discrepancies to tested causes or labels them unresolved; obtain one independent review.
Defend the schema and error policy, demonstrate one repaired bug, and hand the project to a reviewer who can reproduce its outputs.
Defend a representation choice using recorded matched-budget probe/transfer results and a failed transfer case; an embedding visualization alone cannot support the choice.
Defend which architecture solves the synthetic task and identify why that task is weak evidence about natural-language competence.
Write an uncertainty analysis citing the saved simulation results, an effect-size calculation and a multiplicity counterexample; distinguish statistical confidence from practical relevance.
Use a measured convex-versus-neural comparison and an explicit failed assumption to delimit which variance-reduction findings transfer.
Defend an ensemble choice and give a counterexample to interpreting impurity importance as causal influence.
Use measured reconstruction/KL results to defend the tradeoff, diagnose one collapse case and explain with a counterexample why a latent coordinate is not automatically causal.
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