master
3
Commits
| Author | SHA1 | Message | Date | |
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 aleleba
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18efe3fa5d
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Phase 6.4.25b: 13 corrective seeds for gate 5 diagnosis, rebuild training mix
Diagnosis (audited gate5 full-mode transcripts, PNGs, and holdout payloads): the dominant failure mode was NOT rubro/content coverage but a tool-call formatting bug -- after diagnosing an exception, the model often writes its retry as literal <tool_call> XML text embedded in reasoning instead of a structured call, so it's silently dropped (9/10 gate5 full-mode prompts hit this on their final captured turn). Reasoning length before a tool call correlates with the failure (malformed-turn mean 1895 chars vs 364 for well-formed turns), while the training mix's own tool-calling turns never exceed 1630 chars and export_shape turns never exceed 485. Corrective seeds (group D, grupo 'D', short 1-3 sentence diagnosis + immediate well-formed retry, to avoid reinforcing long-reasoning risk): - 9 seeds across fresh rubros (lavanderia, zapateria, optica, jugueteria, papeleria, peluqueria, floreria, heladeria, cerrajeria) covering distinct real API errors: .color on Text, addFlexLayout on non-Board, findShapeById 2-arg, createText() no-arg, textAlign, typography.setFont, createBoolean null, appendChild no-arg, uploadMediaUrl network rejection. - 4 seeds reinforcing uploadMediaUrl over the exact penpot.importImage variant the model actually invents (distinct from the one existing seed, which only taught the penpotUtils.importImage variant) -- rubros: relojeria, guarderia infantil, agencia de viajes, kiosco. Deliberately avoid ferreteria/gimnasio/veterinaria/panaderia: reserved for the held-out generalization probe (separate from the frozen gate 5). Added the newly-verified error strings (t.color, penpot.importImage, addFlexLayout-on-non-Board) to penpot_errors.md -- all captured live from this phase's own gate 5 full-mode run, not fabricated. Rebuilt train_lora2.jsonl (901)/eval_lora2.jsonl (99)/calibration_v2.jsonl from the 138-seed corpus (07_build_lora2_mix.py) and validated (06_validate_dataset.py, 0 exceptions, 0 secrets, max 3271 tokens). |
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aleleba
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ad62277f14
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Phase 6.3: stop the gate depending on penpot.root, and stop blaming the plugin for its own bugs
The setup returned penpot.root.id and the run aborted when it came back as an empty string on the second prompt of a batch. penpot.root is the root of the ACTIVE page, and after createPage plus openPage it need not have caught up yet - a race the page-emptying code introduced, since that leaves the emptied page active. The value was never useful anyway: every page shares the same root id, so it identified nothing. Setup now returns only pageId, which is unique and stable, and both the audit and the cleanup use it. The diagnostic message is the second half of the same mistake. It printed "PLUGIN DEGRADADO, ask the user to reload the browser" whenever any prompt went unmeasured, regardless of why - so it said that for a failure that was entirely the gate's own. A message that sends the user to reload their browser when the bug is mine costs both of us time. It now classifies on the error text: task timeouts and transport drops point at the plugin, anything else points at the gate and says so explicitly. Also closes two evaluation leaks the gate's own pre-flight caught, both in the seeds rather than the gate prompts, since the prompts have to stay as a real user would write them: - A seed shared the 6-gram "la home de una escuela de" with gate prompt 9. My first fix was overwritten by a subagent still writing the file, which is why it reappeared. - A seed used the same business as gate prompt 9 - a music school - without sharing any 6-gram. Shingles cannot see that: two texts describe the same business without sharing words. Training on the domain we then evaluate inflates the result invisibly. So the gate now also checks that no seed uses any of the gate's business nouns, listed explicitly. The training mix was rebuilt: it had been assembled before the 20 ambiguous-brief seeds existed, so training on it would not have used the corpus that was audited. The ambiguous-brief class gets its own mix portion rather than being folded into design, because diluted across 76 design seeds it would be at the mercy of a ratio, and that is the class the user named as the main painpoint. 125 seeds, 168 unique payloads, 446 distinct user prompts, 901 train and 99 eval. |
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aleleba
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c9792c5c40
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Phase 6.3: rewrite the Penpot seed corpus and build the LoRA #2 mix
Replaces the 41 old Penpot seeds with 105 new ones. The old set taught three API forms that do not exist - findShapeById(page, id), shape.layout, and createText() with no argument - and 21 of the 41 used the first one, so patching was never an option: keeping them would mean fine-tuning against the correction. The valuable lessons were re-founded on the real API instead (the reversed children array in flex, persisting ids in storage, never logging what you also return). 105 rather than 96 because nine multi-section compositions are split into two trajectories each: the first builds the skeleton and persists ids, palette, scale and helper functions in storage, the second recovers them and fills the sections. That was forced by the 3000-token ceiling, but it is better pedagogy anyway, and it is what execute_code's own description asks for. It also paid for itself: the helpers cost ~600 chars once instead of twice, and the skeleton call needs no export_shape, which freed the budget to carry the verbatim system block. Measured against the linter with the corpus-wide thresholds active: 143 unique code payloads (the old set had 36), 32% of seeds carrying the server's system block verbatim (the old set had none), and every coverage category met where the old set had zero addGridLayout, zero shadows, zero uploadMediaUrl and zero layoutChild. The flagship changed domain from pizzeria to an empanada shop. Gate 5's prompt 6 is the exact production failure, and the seed had been written to the same wording - a literal substring of the gate prompt, sharing two 6-gram shingles where the gate's disjointness check fails on one. Training on the prompt we then evaluate would make gate 5 measure memorisation. The real pizzeria prompt still runs in the human acceptance test. The same check also caught an onboarding seed sitting too close to gate prompt 7. Mix: 1000 examples split 90/10, giving 901 train and 99 eval. The mix is 1000 rather than 900 because 900 has to be the post-split train size: 900/16 = 56.25 steps per epoch x 3 = 168 steps, deliberately paired with phase 3's 166 so the optimiser trajectory length is comparable. Building 900 and then carving out eval would have left 811 and 152 steps, silently breaking that pairing. Two guards in the builder had to be corrected against the real corpus: - The forbidden-pattern scan now reads only `code` payloads. Scanning the serialised example flagged the corrective seeds themselves - the one that opens with the user asking "Importá esta imagen con import_image", the one quoting the overview line that mentions import_image verbatim, the one explaining that board.layout does not exist. They name the wrong API precisely in order to teach against it. - The exemption for error-recovery seeds is derived from content, not from the mix portion: a forbidden pattern is allowed only where its tool result is a real error string and a later payload does the same thing without it. Keying on the portion broke as soon as an error-recovery seed lived in group A1, where the findShapeById arity seed naturally belongs. Validated with the production tokenizer at preserve_thinking=true: p50 2466, p90 2988, max 3250 tokens over the 105 seeds. Ten sit just above 3000, so MAX_TOKENS goes to 3300 for the run and the smoke run will train on the 32 longest examples specifically, turning the peak-memory question into a ten-minute measurement instead of a risk discovered hours in. |