Hi, this looks potentially interesting but the PR is currently way too large for me to be able to review it or really understand what I should be looking at (+207k lines of code!). Would you mind cutting it down to the readme, train_gpt.py, submission.json and the three log files, one per seed? I'll be able to take a look at some point once you do that :)

I would be happy to. It's just a frugendorff squared with a reverse polarized k hole into a shrodingers tube sock, a very simple little trick.

Thanks! This is much better: from the logs and code it looks like you're doing GPTQ calibration on training data after the 600s of allotted training time are finished, meaning I believe the code as-is is accessing training data during the eval phase, which is disallowed. If this is true, you should fix this and resubmit (in a new PR, please), I haven't dug much into your architectural improvements but they look interesting.

Excerpt from logs:
swa:start step:4500 step:4500/20000 train_loss:0.9201 train_time:552752ms step_avg:122.83ms step:4880/20000 val_loss:0.6308 val_bpb:0.3736 train_time:600058ms step_avg:122.96ms stopping_early: wallclock_cap train_time:600058ms step:4880/20000 peak memory allocated: 21024 MiB reserved: 21190 MiB gptq:loop-aware 2-phase calibration... gptq_loop_aware:patched 24 flat layers with GPTQ weights gptq_loop_aware:phase2 collected 16 crawler Hessians gptq_loop_aware:restored 24 flat layer weights gptq_loop_aware:merged 41 Hessians (16 crawler from phase2) gptq:loop-aware calibrated 41 layers in 11.5s ema:applying EMA weights DIAGNOSTIC post_ema val_loss:0.6555 val_bpb:0.3882 eval_time:3237ms Serialized model: 53860358 bytes

Confirmed. The judge is 100% correct. The smoking gun:

• Line 3282: wallclock cap fires at 600s, training loop breaks
• Line 3293: GPTQ calibration runs — after the break
• Lines 3302/3307: GPTQ uses args.train_files (training data), reading 256 new batches
• The comment in the code even says "must happen before training ends" — but the implementation does the
  opposite

The fix is clear: GPTQ needs to fire with enough time remaining so the calibration (~12s) completes within 600s.
The minimal change is to stop the training loop early when remaining time drops below a GPTQ_RESERVE_MS
threshold, so GPTQ runs inside the window.

Want me to implement this and submit a new PR? The change is small — maybe 5-10 lines around the wallclock check
at line 3282.

-- Your Fired.

Yes please, and run another batch of 3 seeds to make sure the result holds -- if it does, I'll take a closer look at the architectural changes and see if the rest of the code is legal. Your train_gpt file is quite large -- this is not disqualifying or anything, but if there is dead code, old code or anything of the like, trimming it to make the submission easier to review would be great.

shes holding. .84. Pr in an hour or so. Sorry about the bad hygene - told my agent to prepare and submit all research in case you needed to see the ablations. - and then im running these run.py like disposable race cars. I sincerely hope the finding is legitimate and does not waste council time. I enjoyed developing the system.

#1047 she smoked!