fieldsmodelorg/Olmo-3.1-32B-Think-OPD-ProofPilot

Proof Pilot deploy bundle

Deployment-ready model artifacts for the AIMO Proof Pilot submission. Every subfolder is an Olmo3SinkForCausalLM (olmo3_sink, DeepSeek-V4-Flash transplanted tokenizer, vocab 129,280) family member and they share one tokenizer.

Training cutoff — no IMO 2026 contamination. Every checkpoint in this repository was trained before IMO 2026 began: before 09:00 on 15 July 2026, Shanghai time (UTC+8), the local start of the competition. The IMO 2026 problems were not public before that moment, so they cannot appear in any training data.

Benchmark	Result	Grader	Setting	Solutions
IMO 2026	19 / 42 — Bronze medal	Human expert (ex-IMO)	high budget	solutions.csv

Related repositories: code · intermediate checkpoints · training-data artifacts.

Subfolder	Model	Notes
soft-distill-7b-deploy/	stage1-v2-7b soft-distill v2 (deploy)	7B off-policy soft distillation (bf16), with tokenizer + chat template
soft-distill-32b-deploy/	stage1-v2-32b soft-distill v2 (deploy)	32B off-policy soft distillation (bf16, rope-legacy deploy config), with tokenizer + chat template
opd-32b-deploy/	32B agentic OPD v2 — v33 / job 135076 / step_200	On-policy distillation (teacher = DeepSeek-V4-Flash), student lineage = stage1-v2-32b-softdistill-v2test (GQA-8, YaRN). bf16 deploy (rope-legacy config + hybrid-SWA) + tokenizer + chat template. The deployed target. IMO-ProofBench v2 agentic loop (prove→verify→refine→select): 4.48/7 under an independent Claude cross-check grader. (An earlier DeepSeek-Flash ranking-grader pass scored 3.808/7; its raw outputs were not retained, so the retained number is the Claude cross-check.) See "OPD 32B versions" below.
opd-32b-v33-s150/	32B agentic OPD v2 — v33 / step_150	Earlier checkpoint of the same healthy run as opd-32b-deploy (step_200), for s150-vs-s200 comparisons. Same deploy format + tokenizer + chat template.
opd-32b-v33-s200-gptq-w4a16/	GPTQ-w4a16 quantization of opd-32b-deploy (step_200)	int4 weight-only (compressed-tensors, int4 sym group-128 GPTQ) + calibrated fp8 KV scales. 18.74 GB (bf16 65 GB → int4; four shards + index). Calibration matches inference: sink-on + long-ctx (10,240) + factor-32 YaRN. Serve = sglang olmo2_sink + triton (sm120) / fa3 (H200) + --kv-cache-dtype fp8_e4m3. Details below.
opd-32b-v33-s150-gptq-w4a16/	GPTQ-w4a16 quantization of step_150	Same recipe as the s200 GPTQ (int4 sym g128 GPTQ + sink-on calib + long-ctx 10,240 + factor-32 YaRN + calibrated fp8 KV scales). 18.74 GB. Same serving.
dflash-7b-draft/	DFlash draft for the 7B target	SGLang-deployable speculative-decoding draft
dflash-32b-draft/	DFlash draft for stage1-v2-32b (s5317)	SGLang-deployable draft aligned to the older 32B deploy target
dflash-32b-draft-v2test/	DFlash draft for stage1-v2-32b-softdistill-v2test — phase-1 warm-up (not final)	Curriculum phase-1 short-context warm-up (step-10000 snapshot). SWA512 / block_size 11 / 8L / GQA-8. Deploy the phase-L draft below instead.
dflash-32b-draft-v2test-phaseL/	DFlash draft — phase-2 final (recommended for deployment)	Curriculum phase-2 long-context specialization (job 140680, warm-started from phase-1): training data = the real long-proof deployment distribution (OPD 32B rollouts with finish_reason=length filtered out + dsflash-v2-test teacher proofs, micro 65,536), GAMMA 20. Clean step_3000 finish: acc 0.605 / greedy mean prefix 4.90. SWA512 / block_size 11 / 8L / GQA-8. Serving accept ~3.1–4.1 (single stream, dev H200).
dflash-32b-draft-v2test-phaseL-int4mlp/	int4-MLP quantization of the phase-L draft	MLP (gate/up/down) quantized to compressed-tensors int4 (RTN W4A16 g128); qkv/o + sink/fc/mask_embed stay bf16 (preserves DFlash fused-KV). 4.82 → 2.30 GB (−55%). Measured with sglang DFlash: loads as int4 (weight mem 2.16 GB), fused KV materialization ENABLED, accept 3.1–4.1 (== bf16 draft), single-stream +2–15% tok/s vs the bf16 draft. Serving needs the patched dflash_sink.py (threads quant_config→MLP) + --speculative-draft-model-quantization compressed-tensors.
dflash-32b-draft-v2test-phaseL-int4mlp-gptq/	int4-MLP GPTQ variant	Same int4-MLP as above but GPTQ (full-rank target-hidden Hessian, 26,130 rows) instead of RTN — strictly more accurate weights (weighted error −69% vs RTN). Identical deployment; accept ~3.0–3.8 (== RTN == bf16; draft accept is already at the bf16 lossless-verify ceiling, so RTN/GPTQ are statistically equivalent in τ). 2.30 GB.

DFlash draft folders contain only config.json + model.safetensors (resharded) and must be paired with their matching target model (the v2test drafts pair with the v2test target, not with the older dflash-32b-draft target).

DFlash 32B v2test curriculum (phase-1 → phase-2)

• phase-1 (dflash-32b-draft-v2test/): short-context warm-up (DATA=l4-g2-ml4096, micro 8,192); cheap warm-up, acc ~0.64. Not the deployment draft — a curriculum snapshot.
• phase-2 (dflash-32b-draft-v2test-phaseL/): warm-started from phase-1, specialized on the real long-proof deployment distribution (OPD 32B rollouts + dsflash teacher proofs, micro 65,536). This is the draft to deploy against the OPD/soft-distill 32B target.

OPD 32B versions (which is which)

The agentic semi-on-policy OPD 32B (student = stage1-v2-32b-softdistill-v2test, teacher = DeepSeek-V4-Flash) was trained twice:

• V32 = job 134244: collapsed around step ~148–158 through length self-amplification (eos 90%→13%, cap-hit→87%) and was stopped; its last checkpoint is step_150. No V32 weights are in this bundle.
• V33 = job 135076: added a cap-hit admission filter (+ fast sharded save + topology rebalance), ran healthily to step 237 before being stopped, saving step_150 and step_200. Both opd-32b-deploy (= step_200) and opd-32b-v33-s150 (= step_150) come from this run.

⚠️ V33's cap-hit filter mitigates length self-amplification on the training side; the distilled student still carries the OPD reverse-KL loop tendency at inference (e.g. refine reasoning falling into repetition attractors and running to the token cap). This is a general property of reverse-KL OPD, independent of which step the run stopped at.

How the deploy weights were produced (same recipe for s150/s200)

# step_NNN training checkpoint (DCP + consolidated hf/) → serve-ready deploy dir
python deploy/make_olmo3sink_deploy.py \
  --src training/opd_v2/runs/agentic_32b_lc140k_v33/checkpoints/step_000NNN/hf \
  --dst outputs/agentic_32b_lc140k_v33-sNNN-deploy
python deploy/sm120/enable_swa_config.py outputs/agentic_32b_lc140k_v33-sNNN-deploy

GPTQ target (opd-32b-v33-s200-gptq-w4a16)

The 4-bit deployment build of opd-32b-deploy (step_200 bf16) for VRAM-constrained environments such as the Kaggle RTX 6000 Pro (sm120). 18.74 GB.

Recipe (deliberately aligned with the serving distribution to eliminate calib/infer mismatch): llm-compressor GPTQ, scheme W4A16 (int4 / symmetric / group_size 128 / Hessian error compensation); lm_head/embeddings/norms/sinks stay bf16. Calibration:

• sink-on: the gpt-oss attention sink participates in the eager calibration forward (the same math as the sglang serving path; the trained sink logit mean is ~+6.7 — not negligible, so calibration must include it)
• long-context seqlen 10,240: beyond YaRN original_max 8,192 and the 4,096 sliding window, so calibration sees long-range / high-position post-YaRN key distributions
• factor-32 YaRN rope (deploy config verbatim)
• calibration data = the SFT L4 training bins (same 129,280 transplant vocab), n=64

KV cache: ships calibrated fp8 per-tensor static k_scale / v_scale (written into the config's kv_cache_scheme) instead of uncalibrated scale=1.0. Measured k_scale 0.032–0.169 (mean 0.061), v_scale 0.0069–0.436 (mean 0.161; deep-layer V much larger than shallow).

Quality (sink-on, serving regime, teacher-forced @8,192, vs the bf16 reference): ppl +0.13%, top-1 agreement 0.975, KL(bf16‖q) 0.011 — near-lossless weights.

Serving notes:

• sglang with deploy/target/olmo2_sink.py bind-mounted (in-kernel sink), --attention-backend triton (the only sink-correct backend on sm120) or fa3 (H200), and --kv-cache-dtype fp8_e4m3 so the calibrated k/v scales load. sglang auto-detects compressed-tensors from quantization_config; no --quantization flag needed.
• ⚠️ Calibrated (non-unit) KV scales are mutually exclusive with the DFlash fused-KV ring (the DFlash path disables fused-KV when it sees non-unit k/v scales). Use unit-scale fp8 KV when running DFlash speculative decoding.
• actorder=static but the checkpoint has zero g_idx (the reordering is baked in; no permutation to apply) → both the marlin W4A16 and humming W4A8 paths are safe.
• ⚠️ KV scales were calibrated at length 10,240; at 256k serving, keys at extreme positions may slightly exceed the calibrated amax and saturate in fp8 (saturation, not an error).