Dontopedia

Layer 2 Train Loop Integration

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)

Layer 2 Train Loop Integration has 7 facts recorded in Dontopedia across 1 reference.

7 facts·7 predicates·1 sources

Mostly:describes existing loop behavior(1), trigger point(1), reads attribute(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

hasLayerHas Layer(1)

Other facts (7)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

7 facts
PredicateValueRef
Describes Existing Loop Behaviormodel(x) to compute loss[1]
Trigger PointAfter mx.eval[1]
Reads Attributeblock.ffn._last_metrics[1]
Target Scopeeach block[1]
Proposes LoggingJsonl Sidecar[1]
Optional AggregationSystem Beta[1]
Optional Logging Actionlog it as a scalar alongside loss/ppl[1]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

describesExistingLoopBehaviorblah/watt-activation/183
model(x) to compute loss
triggerPointblah/watt-activation/183
After mx.eval
readsAttributeblah/watt-activation/183
block.ffn._last_metrics
targetScopeblah/watt-activation/183
each block
proposesLoggingblah/watt-activation/183
ex:jsonl-sidecar
optionalAggregationblah/watt-activation/183
ex:system-beta
optionalLoggingActionblah/watt-activation/183
log it as a scalar alongside loss/ppl

References (1)

1 references
  1. [1]1837 facts
    ctx:discord/blah/watt-activation/183
    • full textwatt-activation-183
      text/plain3 KBdoc:agent/watt-activation-183/aa64c83c-e728-416e-9b09-62cb1782add2
      Show excerpt
      [2026-03-10 02:09] xenonfun: ``` Key findings: 1. RotationalAdamW: strong convergence from scratch (9.9→7.6 train loss), consistent 11.6K tok/s — expected baseline for cl100k vocab (lower than BPE-8K benchmark's 22K due to 100K-class l

See also

Keep researching

Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.