t2ance/atlas-18-full-benchmark-closing-selector-transfer

ATLAS report 18: does the comparison closing carry ATLAS to the neutral selector on full GPQA and LiveCodeBench?

Complete raw products of ATLAS rl-training report 18 (GitHub issue #39). Three selector surfaces over every question of the canonical LiveCodeBench (175) and GPQA (198) validation sets, each question with all eight of its cached candidates revealed: the current ATLAS forced-submit surface, the same surface with only its closing user message replaced by a candidate-comparison instruction, and a neutral selector shown the same eight candidates with no ATLAS prompt and no tool history. One Qwen3-14B, one greedy deliberative rollout per question per surface, one TP2 vLLM process, 1119 generations.

Start here

report/report18.pdf, the whole write-up. Everything else here is the evidence it cites.

The headline, in short

There is no neutral-to-ATLAS gap to recover at benchmark scale. The neutral selector reads 0.806 on LiveCodeBench against current ATLAS's 0.789 (paired +0.017 [-0.017, +0.051]) and 0.859 on GPQA against 0.848 (+0.010 [-0.015, +0.040]). Report 15's 0.179 gap was a property of a 39-pair probe, not of the benchmarks.

The closing-only surface lands on the neutral value on LiveCodeBench and does not move on GPQA. LiveCodeBench 0.806 (delta vs current +0.017 [-0.011, +0.046], 5 questions gained, 2 lost); GPQA 0.843 (-0.005 [-0.025, +0.010]). On the 39 and 38 mixed questions where a selector decision exists: +0.077 [-0.051, +0.205] and -0.026 [-0.079, 0.000]. Every correctness interval covers zero.

The closing controls reasoning length and raises the correct path mass on LiveCodeBench, without changing decisions. Median think 837 to 2244 tokens (paired +1427 [1090, 1774]); correct path mass 0.754 to 0.811 (+0.056 [0.021, 0.093]); share choosing the first candidate 0.263 to 0.389 (+0.126 [0.063, 0.194]).

Set	Surface	n	Correct	95% interval	Think median
LCB all	neutral	175	0.806	[0.749, 0.863]	3076
	current ATLAS	175	0.789	[0.726, 0.846]	837
	comparison closing	175	0.806	[0.743, 0.863]	2244
LCB mixed	neutral	39	0.795	[0.667, 0.923]	4492
	current ATLAS	39	0.718	[0.564, 0.846]	860
	comparison closing	39	0.795	[0.667, 0.923]	2933
GPQA all	neutral	198	0.859	[0.808, 0.904]	820
	current ATLAS	198	0.848	[0.798, 0.894]	466
	comparison closing	198	0.843	[0.793, 0.894]	611
GPQA mixed	neutral	38	0.632	[0.474, 0.789]	1042
	current ATLAS	38	0.605	[0.447, 0.763]	700
	comparison closing	38	0.579	[0.421, 0.737]	749

Composition of the candidate sets (why the full set is saturated): LiveCodeBench 107 of 175 questions have all eight candidates correct, 26 have none, 9 have every slot timed out; GPQA 145 of 198 all correct, 14 none, and 155 candidate sets carry one answer letter.

The three surfaces

Surface	Directory	What the model sees
current ATLAS	current_atlas_full_set/	frozen system prompt, problem, 8 explore calls with rendered candidates, closing The explore budget is now closed. Submit your final answer now.; decision = full code (LCB) or answer (GPQA) under a prefix-tree grammar over the revealed answers
comparison closing ATLAS	comparison_closing_atlas_full_set/	byte-identical except the closing: The explore budget is closed and no further exploration is available. Analyze the revealed candidate solutions and determine which is most likely to correctly solve the original problem. You may check algorithms, edge cases, examples, and construct counterexamples as needed. After your analysis, submit exactly one of the revealed candidate answers.
neutral	neutral_full_set/	one user turn: the problem, Candidate 1..8 with answer/confidence/approach/reasoning, an instruction to analyze all and reply with one number; decision = the number, read at its first token

File tree

README.md                                   this file
FILES.txt                                   index of every file
report/
  report18.pdf                              THE REPORT, read this first
  report18.tex                              its LaTeX source
manifest.json                               the 373 questions, every candidate's render hash and label, checkpoint file hashes
audits/
  full_set_surfaces.json                    the preflight: surface texts and hashes, every state's conversation hash, prompt token counts
<surface>/qwen3_14b_base/
  <experiment>.jsonl                        373 rows: think text, submit lead, decision tokens, branch points, full conversation
  <experiment>.parquet                      the same rows
  <experiment>.meta.json                    that run's effective configuration; the neutral run's `prompt_arm`,
                                            `closing_sha256` and `closing_text` fields carry the runner's unused
                                            `--prompt-arm` default (the neutral surface has no ATLAS arm), the rows
                                            carry the surface's own arm `neutral` and closing 264f6e02...
trace_packets/
  <surface>.jsonl                           373 deterministic packets per surface
  INDEX.json                                the packet index
tables/
  headline.csv                              per (benchmark, subset, surface) metrics
  paired_delta.csv                          paired differences: closing, residual, gap
  matrix.csv                                the 2x2 correctness counts
  positions.csv                             selected-position histograms
  composition.csv                           subset sizes
  states.csv                                the per-question join of the three surfaces
  rows.csv                                  every scored row
  trace_index.csv                           the trace cohorts
  trace_reading_lcb.md, _gpqa.md            the reading of the 21 cohort questions, three surfaces each
smoke/                                      the smoke test's rows (2 LCB + 2 GPQA per surface)
logs/
  serve_qwen3_14b_base_tp2.log              the vLLM server log
  run_<surface>.log                         one log per run

How to read a file

A text file is served at https://huggingface.co/datasets/t2ance/atlas-18-full-benchmark-closing-selector-transfer/raw/main/<path>.

A .parquet is stored through LFS, so raw/ returns only a small pointer. Use resolve/main/<path> instead. FILES.txt is the complete index; do not rely on tree/main?recursive=true, which pages.

Trace packets and the recorded reading

trace_packets/<surface>.jsonl places, per question: the correct positions and the answer-bearing positions, the selected position and whether it was correct, the path mass (ATLAS) or first-token distribution (neutral), the full think text, the submit-lead text, the selected answer, and the first branch point where several codes were still reachable. Nothing in them classifies the reasoning.

tables/trace_reading_lcb.md and tables/trace_reading_gpqa.md record a reading of 21 questions, three surfaces each: every closing rescue and closing break (LCB 7, GPQA 3), the whole neutral-and-closing-against- current cohort (8, all among the former), and 16 hard mixed questions (LCB 12, GPQA 4 further). Each file opens with a summary table, then one section per question: what each surface's reasoning did, with quoted excerpts, whether the comparison closing produced a concrete check the current surface did not, whether that check contained a wrong step, and whether the submission followed the reasoning. The classification of the closing relative to the current prompt uses verification_helped, verification_erred, preference_only, mismatch, unchanged, and runaway_lead; the totals are 3, 0, 5, 1, 10, and 2.

Provenance

• Model: Qwen/Qwen3-14B, snapshot 40c069824f4251a91eefaf281ebe4c544efd3e18, untrained base weights, bf16.
• Serving: vLLM, --tensor-parallel-size 2 over 2x A100 80GB, max_model_len 32768, prefix caching on, 743,840-token KV cache, 54.7 percent prefix-cache hit rate.
• Decoding: greedy, temperature 0. Think budget 8192 tokens, submit-lead and decision budgets 6144 tokens.
• All three runs shared the one server process; 48 workers each; 08:37 to 09:48 UTC on 2026-09-04.
• Code: t2ance/ATLAS, branch feature/full-set-closing-transfer, Experiment/core_code/scripts/decision_value/.
• Companion datasets: t2ance/atlas-17-prompt-interpolation-selector-recovery (report 17), t2ance/atlas-16-verifier-permission-prompt-ablation (report 16), t2ance/atlas-15-selector-capacity-vs-training (report 15).