Merge dev4 into main

Removed redundant descriptions and clarified sections in the README.

README.md

@@ -98,7 +98,7 @@ When you're stuck after a diagnostic cycle or two, the generalization of this ad
 
 > What I'm advocating for here is not a blind faith in the buginess of your code, but for dramatically raising the threshold at which you start thinking 'OK, I think this is correct.'[^jones]
 
-A bug can also hide, because most ML models have multiple adaptive parts: 
+A bug can also hide, because most ML models have multiple adaptive parts:
 
 > "If one part is broken, the other parts can adapt and still achieve roughly acceptable performance" [^goodfellow],
 and it may not show in the output at all.
@@ -356,8 +356,8 @@ instead.
 From William Falcon's attendee notes on Schulman's talk, so a secondary source rather than
 Schulman's own text[^deeprlhacks]:
 
-> 4. Think your algorithm is working but you're actually seeing random noise.   
+> 4. Think your algorithm is working but you're actually seeing random noise.
->     - Example: Graph of 7 tasks with 3 algorithms and looks like 1 algorithm might be doing best on all problems, but turns out they're all the same algorithm with DIFFERENT random seeds.   
+>     - Example: Graph of 7 tasks with 3 algorithms and looks like 1 algorithm might be doing best on all problems, but turns out they're all the same algorithm with DIFFERENT random seeds.
 
 Nanda on why no internal warning fires:
 
@@ -486,7 +486,7 @@ reports his two real bugs on that environment were a terminal-flag masking error
 broadcast, neither of which any of these can reach[^reddit-rl]:
 
 > Things I've tried (but maybe not systematically enough):
-> 
+>
 > * Different initial LRs
 > * Different optimizers
 > * Different number of hidden layers/units
@@ -512,7 +512,7 @@ wanted (the "demon-strated" break is an OCR artifact in the cached copy):
 notes[^deeprlhacks]:
 
 > 2. Make sure observations usable:
->     - See if YOU could control the system by using the same observations you give the agent.   
+>     - See if YOU could control the system by using the same observations you give the agent.
 >       - Example: Look at preprocessed images yourself to make sure you don't remove necessary details or hinder the algorithm in a certain way.
 
 For LLM work, the data you have to read is the tokenized data:

docs/candidate_quotes.md

@@ -36,8 +36,8 @@ If either mode matters most to you, this cache needs a new source, not more mini
 - failure modes: 1
 - epistemic context: secondary source, attendee notes on Schulman's talk rather than Schulman's own text; the primary slide deck is cached separately as joschu_nuts_and_bolts.md.
 
-> 4. Think your algorithm is working but you're actually seeing random noise.   
+> 4. Think your algorithm is working but you're actually seeing random noise.
->     - Example: Graph of 7 tasks with 3 algorithms and looks like 1 algorithm might be doing best on all problems, but turns out they're all the same algorithm with DIFFERENT random seeds.   
+>     - Example: Graph of 7 tasks with 3 algorithms and looks like 1 algorithm might be doing best on all problems, but turns out they're all the same algorithm with DIFFERENT random seeds.
 
 Why it lands: a confident cross-task ranking read off three copies of one algorithm. It is the shortest demonstration that a conclusion can feel fully supported by a plot and be supported by nothing.
 
@@ -287,7 +287,7 @@ Why it lands: sweeping the legible knobs is brute-force search wearing a lab coa
 - epistemic context: LOW CREDIBILITY. Anonymous reddit self-report from a self-described non-expert. Its value is as a specimen of the failure mode, not as advice, and it should not be quoted as authority.
 
 > Things I've tried (but maybe not systematically enough):
-> 
+>
 > * Different initial LRs
 > * Different optimizers
 > * Different number of hidden layers/units
@@ -328,7 +328,7 @@ Why it lands: a healthy-looking curve produced by a swimmer curling up and flail
 - epistemic context: secondary attendee notes; the matching primary slide is "Atari: can you see game features in downsampled image?" in the cached joschu_nuts_and_bolts.md.
 
 > 2. Make sure observations usable:
->     - See if YOU could control the system by using the same observations you give the agent.   
+>     - See if YOU could control the system by using the same observations you give the agent.
 >       - Example: Look at preprocessed images yourself to make sure you don't remove necessary details or hinder the algorithm in a certain way.
 
 Why it lands: turns "read the data" into a pass/fail test that takes a minute. If you cannot do the task from the model's inputs, no hyperparameter will save it.