docs: fix typos in experiment log (#547)

dev/LOG.md

@@ -749,7 +749,7 @@ See the branch `fp8_attempt_fail` for:
 ### Open Questions
 
 - Why does the custom op approach use more memory than vanilla BF16?
-- Why is the bump in tok_per_sec so low? We should see ~1.6X speedup in both the forward pass and also (twice) in backward pass for the gradients. Granted, Ahmdal's law is part of the solution because our vocab_size is only 32K so the final layer isn't a huge part of the profile but the expected speedup is still not fully realized.
+- Why is the bump in tok_per_sec so low? We should see ~1.6X speedup in both the forward pass and also (twice) in backward pass for the gradients. Granted, Amdahl's law is part of the solution because our vocab_size is only 32K so the final layer isn't a huge part of the profile but the expected speedup is still not fully realized.
 
 **Conclusion:** Negative result for now. The implementation works correctly but provides marginal speedup with *increased* memory usage. I'm not understanding the torch.compile interaction here. The complexity of FP8 custom ops isn't justified for lm_head alone. TODO to study in more detail the way this is implemented in other libraries, e.g. torchao.
 
@@ -913,7 +913,7 @@ Cherry-picked improvements from NorMuon (modded-nanogpt) into our simpler Muon i
 - Now defaults to ON for Muon via the `weight_decay` param. AdamW still has no weight decay and is hardcoded to 0 weight decay, might try to re-tune this later.
 
 **4. Weight decay schedule**
-- Added a linear schedule to weight decay that is default on from 1.0 to 0.0 (i.e. start with max weight decay in the beginning of training, them ramp to 0 by the end). Worked better than a static setting in experiments. (modded-nanogpt has the same schedule but it is imlpemented in a more confusing way by multiplying twice by the learning rate, which is already wired up to a decay schedule).
+- Added a linear schedule to weight decay that is default on from 1.0 to 0.0 (i.e. start with max weight decay in the beginning of training, then ramp to 0 by the end). Worked better than a static setting in experiments. (modded-nanogpt has the same schedule but it is implemented in a more confusing way by multiplying twice by the learning rate, which is already wired up to a decay schedule).
 
 ### Weight Decay Scaling Experiments
 
@@ -957,6 +957,6 @@ Muon was changed to use Polar Express, added NorMuon variance reduction, and cau
 
 **Bug Found:** Original implementation clipped local gradients before sync. Since this codebase doesn't use DDP (gradient sync is in the optimizers), each rank was clipping based on its own local norm. Fixed on the branch with proper distributed all-reduce.
 
-**Observartion:** modded-nanogpt does not appear to clip either right now.
+**Observation:** modded-nanogpt does not appear to clip either right now.
 
 **Summary:** Deleted all grad-clip code paths. The code naturally produces well-behaved gradients. This improves a bit of MFU because we don't have to calculate and sync grad norms.