atrost/agentic-rl-transfer

Qwen3.5-35B-A3B — v0.9 dataset

Last substantive update: 2026-08-04

v0.9 is v0.7 with 38,145 assistant turns excluded from the loss. Nothing else differs — same rows, same order, same content, same reasoning, same pack seed.

The design and its evidence are in QWEN35_V0_9_DATASET_PLAN.md. This file records what was actually built, where it is, and how to check it.

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Location — b200 cluster

/NHNHOME/WORKSPACE/wbl-workspace/agentic-rl-260803/datasets/v0.9/
  raw/v0_9_final.jsonl              742,665 rows, 34,528,993,378 bytes
  packed-65k/train/                 137,384 packs in 8 parquet shards
  provenance/mask-flags-v1.jsonl    38,145 records, one per masked turn
  provenance/v0_9_build_report.json
  provenance/v0_9_verify_report.json
  provenance/census_v0_9_v2.json
  provenance/*.py, *.sbatch         the exact scripts that produced it
  logs/

artifact	SHA-256
base v0.7/raw/v0_7_final.jsonl	cd94b138f7988b81c48e12ff3b75559192ef7c4b48fdc2f86d076e48d830266d
raw/v0_9_final.jsonl	7f742b2b3c3ab569c007070ca7d17089cc03d1cf4fa9375cccd58dc305b62a2f
staged v0.8 classifier flags	a5d2f1f5999e7f03b495ff3e389656574c89a4e2830cb1e7bac3d0777299a0c1

Superseded artifacts from the abandoned --claim-policy correct build are kept under superseded-correct-policy/; they are not inputs to anything.

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What is masked

38,145 turns across 33,291 rows — 5.1% of assistant turns in 4.5% of rows. Reasons overlap (most stalls are also malformed JSON), so each masked turn carries the set of reasons it qualifies for. The per-reason totals sum to 66,420; the union is 38,145.

• 32,836 terminus-json — the Terminus-2 harness rejected the action object
• 28,292 stall — empty content (28,270), no commands key (20), empty mini-swe content (2)
• 3,116 false-claim — final assistant turn asserting task_complete: true on a row a trusted verifier failed (numeric non-bool reward, explicit passed, no infra exception, not a known-broken verifier family). 84 further candidates also carried an executable command and were left alone
• 1,385 toolcall-syntax — malformed or schema-invalid tool call; 484 land on the general-agent sources τ³ measures (API-Bank 478, Toucan 5, ToolACE 1)
• 791 prose-command — mini-swe turns that write a command as prose instead of executing it (727 of 738 no-toolcall, 64 of 305 trailing-after-submit)

Combination counts: stall+terminus-json 28,275 · terminus-json alone 4,561 · false-claim 3,116 · toolcall-syntax 1,385 · prose-command 791 · stall alone 17.

What is deliberately left trained

• 9,692 rewrites — v0.7 has no masks and this is the most error-reactive class (58.66%), while τ³ error recovery is the known weakness
• 252 prose-only mini-swe turns — end-of-run summaries with no command text; v0.7 trained them normally
• 200 terminus-unparsable-harness-rejected turns that carry no terminus-json class. The other 4,264 unparsable turns do carry it and are masked
• 796 rows that passed contain 819 masked turns. This is intended: a stall inside a trajectory that eventually passed is still a stall

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How it was built

workflows/sft/v0_9/census_v0_9.py     measure only, never mutates
workflows/sft/v0_9/build_v0_9.py      one streaming pass over the base
workflows/sft/v0_9/verify_v0_9.py     independent on-disk re-check
workflows/sft/v0_9/build_v0_9.sbatch  b200 launcher (1:56 wall)
workflows/sft/v0_9/pack_v0_9.sbatch   b200 pack at 65,536

Packed with the same artifacts that produced v0.7 and v0.8 on b300 — packer 976ce4bb…, tokenizer.json 06b95093…, chat_template.jinja f8a27a12…, staged at agentic-rl-260803/env/qwen35-qwen36-thinking-generation-spans. A 300-row sample tokenizes to an identical digest on both clusters (1594220b35a637c8…; transformers 5.8.1, tokenizers 0.22.2, megatron-bridge 0.5.0 on each), so the b200 pack is not a different rendering of the corpus.

Seed 20260731 and 8 shards, matching v0.7, on v0.7's row order — shard membership is identical, so a v0.7↔v0.9 comparison is not confounded by a different packing.

Pack result, against v0.7

	v0.7	v0.9	delta
rows in	742,665	742,665	0
rows packed	742,661	742,656	−5
packs	137,387	137,384	−3
tokens	8,978,973,597	8,978,945,130	−28,467
packing efficiency	99.71%	99.73%	—
rows over 65,536 tokens	4	4	0
one epoch @ GBS16	—	8,587 steps	—

The whole difference is 5 rows, and they are accounted for. rows_over_max_len is 4 in both, which independently confirms no content grew or shrank; the extra 5 dropped rows are rows_fully_masked — trajectories in which every assistant turn was a defect, so nothing trainable survived. The packer drops those rather than emit a zero-loss sample.

Loss coverage: 54,577,131 masked tokens against 3,408,327,232 trainable — masking removes 1.58% of the assistant-target loss. Span integrity was clean: anomalies=0, turn_flags_on_non_assistant=0, span_turn_count_mismatch=0, span_bad_start=0, span_multiple_headers=0, so every mask landed on the token span it was meant to. The validator additionally sampled 128 packs and read back 3,001,541 assistant loss tokens.

Gates that passed

Build (v0_9_build_report.json):

• base and flag digests re-checked at build time; output digest recorded
• 742,665 rows in, 742,665 out; no rows added, dropped or reordered
• every modified row compared against source-plus-declared-changes — an unintended edit anywhere is a nonzero exit; 0 occurred
• 0 alignment anomalies: every flag resolved to an assistant turn
• per-reason counts equal to the census figures, exactly
• 4,963/4,963 untouched-row probes re-serialise byte-identically

Verify (v0_9_verify_report.json), separate logic reading both files off disk:

• 0 content corrections seen — the only field that changed anywhere is step_loss_mask
• the base carried no step_loss_mask of its own, so all 38,145 are this build's
• every masked turn's source content still matches the fingerprint recorded at build time
• every false-claim mask is the final assistant turn of a passed == False row whose parsed object carried task_complete: true
• 0 errors

Pack (packed-65k/validation-summary.json), validate_qwen35_packed_dataset.py:

• 742,656 of 742,665 samples present, 9 dropped and both causes accounted for
• 137,384 packs at 65,536 tokens, 99.7262% efficiency
• 0 span anomalies of any kind; masks land on the intended token spans

Training

3 epochs, TP2/EP8, MBS1/GBS16, LR 1e-5/1e-6, save every 500 steps. One epoch is 8,587 steps (137,384 packs / GBS 16) — the same as v0.7, whose 137,387 packs also round to 8,587 — so matched-step comparisons against v0.7 are exact. Two epochs is iter 17,174 and three is 25,761; with saves every 500, the nearest checkpoints are 17,000 and 25,500. Point packed_data_path at datasets/v0.9/packed-65k. Evaluation contract, hard floors and the checkpoint-selection rule are predeclared in QWEN35_V0_9_DATASET_PLAN.md §5 — read it before scoring, not after.