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zz1358m/Qwen3-30B-A3B-Agentic-ESOpt-Math
Qwen3-30B-A3B-Agentic-ESOpt-Math is a machine learning model from zz1358m. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This repository contains the exact tensor-parallel state produced by the 20-update full-parameter Agentic-ESOpt Math run.
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From the Hugging Face model README
This repository contains the exact tensor-parallel state produced by the 20-update full-parameter Agentic-ESOpt Math run.
Checkpoint correction (2026-09-18): The checkpoint files initially uploaded to this repository were exported incorrectly and did not reproduce the trained TP=2 runtime state. The TP=2 vLLM sharded-state checkpoint uploaded on 2026-09-18 is the correct checkpoint.
| Benchmark | Mean4 | Pass4 |
|---|---|---|
| DAPO | 50.0 | 74.0 |
| AIME2026 | 28.3 | 46.7 |
The source evaluation generated 16 samples per problem. The reported
Mean4/Pass4 row is the group with sample_index 12–15. Mean4 is the mean score
over those four samples; Pass4 is the mean per-problem maximum over those four
samples. These remain the original Eval16 results; the later aligned
four-sample replay is included as a reproduction artifact and does not replace
the reported scores.
The checkpoint is a vLLM sharded-state checkpoint and must be loaded with tensor parallel size 2:
from vllm import LLM
llm = LLM(
model="zz1358m/Qwen3-30B-A3B-Agentic-ESOpt-Math",
tokenizer="Qwen/Qwen3-30B-A3B",
load_format="sharded_state",
tensor_parallel_size=2,
dtype="bfloat16",
trust_remote_code=True,
)
Do not load this checkpoint with transformers.AutoModelForCausalLM, TP=1,
or a tensor-parallel size other than 2. Full-parameter ES used independent,
rank-salted random streams on the two TP workers. Some vLLM parameters are
replicated across TP ranks, so the exact state cannot be represented by one
ordinary unsharded Hugging Face state dict.
ddof=0)es_sharded_export_manifest.json records the source history hash, replayed
generation count, required load format, TP size, and emitted worker shards.
repro/dapo_eval16.json: original DAPO 16-sample evaluation recordsrepro/aime2026_eval16.json: original AIME2026 16-sample recordsrepro/metrics.json: original reported Eval16 metrics and aggregationrepro/es_history_theta20.json: exact history used for reconstructionrepro/aligned_history_replay_eval4.json: aligned four-sample replayrepro/SHA256SUMS: published-file checksums