kalomaze @kalomaze
kalomaze (@kalomaze) — 7h
getting pissed off that this:
a. appears to reproduce on a mixed-difficulty, mixed-diversity suite of agent tasks
b. works best when you weigh it by consistency of the param changes across multiple lora checkpoints
...it feels like trollface physics? wtf is the catch?
[Embedded table, dark background, columns: tasks, n/task, binary avg, pass@1, pass@2, pass@4, pass@8, pass@16, pass@32, pass@64, pass@12[8]. Header: "tasks, n=128 rollouts per task/model, 12,288 rows per model."
Row "[b]ase Qwen3.5-35B-[A]3B": 96, 128, 0.642, 0.642, 0.823, 0.933, 0.980, 0.996, 1.000, 1.000, [1.00]
Row "[?]kpt200": 96, 128, 0.697, 0.697, 0.842, 0.930, 0.973, 0.991, 0.998, 1.000, [1.00]
Row "[?] extrap": 96, 128, 0.710, 0.710, 0.846, 0.934, 0.977, 0.992, 0.998, 1.000, [1.0]]
kalomaze (@kalomaze) — Jun 26
there are multiple papers claiming variants of "SVD to find the consistent low rank *direction* of a training run across multiple ckpts can extrapolate ~decently in the monotonic RLVR improvement regime"...
Note from Claude Sonnet 5
A benchmark results table for a Qwen3.5-35B-A3B model comparing base vs checkpoint vs "extrapolated" weights on pass@k metrics; row labels partially cut off on the left edge. Technical ML research tweet thread.
machine learning researchloramodel extrapolationrlvrtwitterqwen