DoReMi
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
DoReMi has 78 facts recorded in Dontopedia across 24 references, with 5 live disagreements.
Mostly:has training step(18), uses dataset(6), processes dataset(6)
Maturity scale
raw canonical shape-checked rule-derived certifiedHas Training Stepin disputehasTrainingStep
- Training Step 1900[17]all time · Part 711
- Step 1750[17]all time · Part 711
- Training Step 1760[17]all time · Part 711
- Training Step 1770[17]all time · Part 711
- Training Step 1780[17]all time · Part 711
- Training Step 1790[17]all time · Part 711
- Training Step 1800[17]all time · Part 711
- Training Step 1810[17]all time · Part 711
- Training Step 1820[17]all time · Part 711
- Training Step 1830[17]all time · Part 711
Inbound mentions (15)
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.
partOfRunPart of Run(2)
- Training Step 1750
ex:training-step-1750 - Training Step 1760
ex:training-step-1760
basedOnBased on(1)
- Teacher in Loop Agent
ex:teacher-in-loop-agent
capsDoReMiByDomainSizeCaps Do Re Mi by Domain Size(1)
- Direction 1
ex:direction-1
combinesCombines(1)
- Item 4
ex:item-4
concernsConcerns(1)
- Log Entry
ex:log-entry
involvesFeatureInvolves Feature(1)
- Item 4 Order
ex:item-4-order
letsDoReMiReweightLets Do Re Mi Reweight(1)
- Direction 1
ex:direction-1
mentionsSubjectMentions Subject(1)
- Log Entry 2026 05 02 01 01
ex:log-entry-2026-05-02-01-01
reportsProgressOfReports Progress of(1)
- Log Entry
ex:log-entry
restoredCleanlyRestored Cleanly(1)
- Resumed Run
ex:resumed-run
restoredStateRestored State(1)
- Resumed Run
ex:resumed-run
signalsHigherLossSignals Higher Loss(1)
- ↑ Ranking
ex:↑-ranking
usesMethodUses Method(1)
- Unified Rotor Gpu 25m
ex:unified-rotor-gpu-25m
worksAsDesignedWorks As Designed(1)
- Unified Rotor Gpu 25m Doremi
ex:unified-rotor-gpu-25m-doremi
Other facts (57)
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.
| Predicate | Value | Ref |
|---|---|---|
| Uses Dataset | Classics Literature | [17] |
| Uses Dataset | General Knowledge | [17] |
| Uses Dataset | Practical Texts | [17] |
| Uses Dataset | Professional Benchmark | [17] |
| Uses Dataset | Reference Web | [17] |
| Uses Dataset | Speculative Fiction | [17] |
| Processes Dataset | General Knowledge | [24] |
| Processes Dataset | Reference Web | [24] |
| Processes Dataset | Classics Literature | [24] |
| Processes Dataset | Professional Benchmark | [24] |
| Processes Dataset | Practical Texts | [24] |
| Processes Dataset | Speculative Fiction | [24] |
| Rdf:type | Software Component | [20] |
| Rdf:type | Training Module | [21] |
| Rdf:type | Algorithm | [22] |
| Rdf:type | Weighting Algorithm | [23] |
| Rdf:type | Model Run | [24] |
| Is Known Technique | Curriculum Training | [1] |
| Included in | Item 4 | [2] |
| Is Known Method | true | [3] |
| Is Weight Update Method | Domain Adaptation | [4] |
| References Optimization Technique | Doremi Weights | [5] |
| Bets on Domain Sensitivity | null | [6] |
| Referenced in Context | null | [6] |
| Reweighted After Step | 1000 | [7] |
| Has Designed Functionality | Dataset Weighting | [8] |
| Heavily Weighted on Hard Domains | true | [9] |
| Up Weights Harder Ones | true | [10] |
| Creates Feedback Loop | true | [10] |
| Presupposed As Training Method | true | [10] |
| Deontically Required for Hard Domain Handling | true | [10] |
| Sees More Per Domain Loss | true | [10] |
| Restorable Cleanly | True | [11] |
| Causes Tiny Domain Overexposure | Tiny Domains | [12] |
| Adaptive Weighting | true | [12] |
| Reweights Domains | Domains | [12] |
| Is Integrated Component | null | [13] |
| Indicates Trend Up | Poetry Dataset | [14] |
| Indicates Trend Down | Simple Educational Dataset | [14] |
| Reacts to | Overexposure | [15] |
| Prioritizes Harder Data | true | [15] |
| Has Started Lowering | Professional Benchmark | [15] |
| Correctly Adjusts Weights | true | [15] |
| References Domain Reweighting Method | prior technique | [16] |
| Presupposes Existence | true | [16] |
| Computes | Raw Domain Weights | [16] |
| Maintains Constant Learning Rate | 0.34 | [17] |
| Can Upweight | Hard Domains | [18] |
| Handles Domain Weighting | True | [18] |
| Is Sampling Method | True | [18] |
| Prediction Hypothesis | domain-sensitive AND fluent | [19] |
| Can Upweight Hard Domains | true | [22] |
| Capable of | noticing hard/noisy tiny domains | [23] |
| Limited by | Exposure Governor | [23] |
| Has Training Start Step | 1750 | [24] |
| Has Training End Step | 1920 | [24] |
| Uses Exposure Governor | Sampling Weights After Exposure Governor | [24] |
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.
References (24)
ctx:discord/blah/watt-activation/part-634ctx:discord/blah/watt-activation/part-640ctx:discord/blah/watt-activation/part-645ctx:discord/blah/watt-activation/part-662ctx:discord/blah/watt-activation/part-668ctx:discord/blah/watt-activation/part-670ctx:discord/blah/watt-activation/part-680ctx:discord/blah/watt-activation/part-682ctx:discord/blah/watt-activation/part-687ctx:discord/blah/watt-activation/part-686ctx:discord/blah/watt-activation/part-702ctx:discord/blah/watt-activation/part-703ctx:discord/blah/watt-activation/part-704ctx:discord/blah/watt-activation/part-701ctx:discord/blah/watt-activation/part-705ctx:discord/blah/watt-activation/part-707ctx:discord/blah/watt-activation/part-711ctx:discord/blah/watt-activation/part-706ctx:discord/blah/watt-activation/667ctx:discord/blah/watt-activation/699- full textwatt-activation-699text/plain3 KB
doc:agent/watt-activation-699/23eaed18-1273-410e-98e4-c3f03fb91004Show excerpt
[2026-05-01 05:43] xenonfun: ``` The universal shaft of the state was the most empty object in the next step. The explanatory phenomenon itself is associated with the physical-phenomenon of the two thin fibrosis residues of the theoretical …
ctx:discord/blah/watt-activation/698- full textwatt-activation-698text/plain3 KB
doc:agent/watt-activation-698/f8b43921-7946-4fa9-adc2-3852b2c781f2Show excerpt
[2026-05-01 04:58] xenonfun: ``` Checked it out. My take: the handoff is strong and mostly accurate, but there are two path mismatches worth fixing. e23 training is healthy and still running. Latest metric I saw was step `4350 / 25000`, ab…
ctx:discord/blah/watt-activation/703- full textwatt-activation-703text/plain3 KB
doc:agent/watt-activation-703/c2aff1b4-7f23-419c-b531-687b267a9440Show excerpt
[2026-05-01 20:40] xenonfun: 1K smoke finished and inference is done. Run: `/home/ms/data/clifford_ssm/checkpoints/e24_chinchilla_from_e23_best_prefetch_1k/latest.pt` Final training stats: - step `1000` - loss / BPB: `0.9332 / 1.3463` - t…
ctx:discord/blah/watt-activation/706- full textwatt-activation-706text/plain2 KB
doc:agent/watt-activation-706/6633be5c-fab3-495f-b5b5-8dadec7f0fbbShow excerpt
[2026-05-01 23:01] xenonfun: Yes. The 5K gate run is launched and behaving well so far. Current run: `e24_chinchilla_latest_governed_5k` It is warm-started from e23 best with: - `B=20`, `T=2048` - updated Chinchilla dataset, `6,123,670` t…
ctx:discord/blah/watt-activation/708- full textwatt-activation-708text/plain1 KB
doc:agent/watt-activation-708/43ecf53f-d65d-4ffb-8caf-9d1e36a76aeaShow excerpt
[2026-05-02 01:01] xenonfun: ``` [doremi] step=1750 sampling weights after exposure governor ↑ general_knowledge w=0.162 raw=0.094 ema_loss=1.087 ↑ reference_web w=0.158 raw=0.091 ema_loss=1.059 ↑ …
See also
- Curriculum Training
- Item 4
- Domain Adaptation
- Doremi Weights
- Dataset Weighting
- True
- Tiny Domains
- Domains
- Poetry Dataset
- Simple Educational Dataset
- Overexposure
- Professional Benchmark
- Raw Domain Weights
- Training Step 1900
- Step 1750
- Training Step 1760
- Training Step 1770
- Training Step 1780
- Training Step 1790
- Training Step 1800
- Training Step 1810
- Training Step 1820
- Training Step 1830
- Training Step 1840
- Training Step 1850
- Training Step 1860
- Training Step 1870
- Training Step 1880
- Training Step 1890
- Training Step 1910
- Training Step 1920
- Classics Literature
- General Knowledge
- Practical Texts
- Professional Benchmark
- Reference Web
- Speculative Fiction
- Hard Domains
- Software Component
- Training Module
- Algorithm
- Weighting Algorithm
- Exposure Governor
- Model Run
- Sampling Weights After Exposure Governor
- General Knowledge
- Reference Web
- Classics Literature
- Practical Texts
- Speculative Fiction
Keep researching
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