license: mit
tags:
- clara
- reasoning
- pretraining
- synthetic
Clara_v4_stage3 — targeted reasoning
172,282,390 tokens · 4 pools · pre-tokenised uint16 bins. Each pool is aimed at a measured
weakness, not a guessed one. Measured on clara_1920_lockedmix (d24, n_embd 1920, dense) at step
72,000, CORE corrected:
| pool | tokens | target task | measured centered score |
|---|---|---|---|
letter_binding_v1 |
75,499,631 | commonsense_qa |
−0.0625 (below chance) |
deduction_chains_v1 |
63,400,968 | agi_eval_lsat_ar |
−0.0029 |
dyck_ops_v1 |
19,892,808 | bigbench_dyck_languages |
0.1520 |
repeat_ops_v1 |
13,488,983 | bigbench_repeat_copy_logic |
0.0000 (0/32) |
Three of the model's four weakest CORE tasks; the fourth
(bigbench_language_identification) is deliberately unserved.
Format
uint16 little-endian. BOS = 65463, EOS = 65464. Tokenizer tok_64k_singledigit, sha256
0c9c6d8c528d5aae542e27276ef9c48000dc32a8e3edab76071438d3be40d706, Digits pre-tokenizer stage
present. Verify with CLARA_TOKENIZER/verify_tokenizer.py from Clara_v4_stage1 before mounting.
Dosing — for COVERAGE, not presence
Under random-offset sampling a pool served at N epochs shows only 1 − e^(−N) of itself:
1 ep = 63%, 3 ep = 95%, 5 ep = 99.3%. A targeted pool must be dosed for coverage — ≥3 epochs —
or a third of it is never drawn. On a 115.8B run that is 0.196% / 0.164% / 0.052% / 0.035%.
⚠️ Ruler discipline
Every pool here targets a named benchmark task and is therefore a contamination magnet.
letter_binding_v1 and deduction_chains_v1 carry import-time disjointness asserts against their
probe banks. ⛔ repeat_ops_v1 shares vocabulary with 5 of repeat_copy_logic's 32 items — that
is 0.156 of the task, so a gain of ≤5/32 on that task is not evidence the pool worked.
⛔ The eval bundle and every probe file are never training inputs. Pool/ruler crossing has happened four times on this program; the only safe direction is to move the pool, never the ruler.
Provenance
Entirely synthetic, generated by this lab. Items are produced by actually solving, never by
templating a line per input — deduction_chains_v1 solves by constraint propagation and then
backward-slices to the inferences the answer depends on.
Shards: 65 / 64 / 49 / 49. Sharded so n_shards >= world_size x 4; with shards[rank::world] a pool with fewer shards than ranks leaves some ranks with zero shards for that lane and it is silently served at a fraction of its configured dose.