vinoku89
/

aimo3-exp-1

+W&B Project: aimo3-exp-1-val
+W&B Run: qwen3-0.6b-sft-lora-20260116_220920-004
+wandb: Currently logged in as: vinoku (vinokuteam) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
+wandb: WARNING Using a boolean value for 'reinit' is deprecated. Use 'return_previous' or 'finish_previous' instead.
+wandb: setting up run jo6acl31
+wandb: Tracking run with wandb version 0.23.1
+wandb: Run data is saved locally in /workspace/distributed-sft-rl-inference-pipeline/wandb/run-20260116_141237-jo6acl31
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run qwen3-0.6b-sft-lora-20260116_220920-004
+wandb: ⭐️ View project at https://wandb.ai/vinokuteam/aimo3-exp-1-val
+wandb: 🚀 View run at https://wandb.ai/vinokuteam/aimo3-exp-1-val/runs/jo6acl31
+wandb: Detected [openai] in use.
+wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
+wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
+W&B Run: qwen3-0.6b-sft-lora-20260116_220920-004
+W&B URL: https://wandb.ai/vinokuteam/aimo3-exp-1-val/runs/jo6acl31
+Using vLLM for high-throughput inference
+Auto-detected 1 GPU(s) for tensor parallelism
+Cleaning GPU memory before vLLM initialization...
+GPU memory cleaned
+============================================================
+Loading Model with vLLM
+============================================================
+Base model: Qwen/Qwen3-0.6B
+Tensor parallel: 1 GPU(s)
+GPU memory utilization: 90%
+Max model length: 4096
+Max num seqs: 128
+Dtype: half
+The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
+WARNING 01-16 14:12:51 [model.py:2005] Casting torch.bfloat16 to torch.float16.
+WARNING 01-16 14:12:58 [system_utils.py:136] We must use the `spawn` multiprocessing start method. Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing for more information. Reasons: CUDA is initialized
+[0;36m(EngineCore_DP0 pid=5136)[0;0m
+Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
+[0;36m(EngineCore_DP0 pid=5136)[0;0m
+Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:14<00:00, 14.54s/it]
+[0;36m(EngineCore_DP0 pid=5136)[0;0m
+Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:14<00:00, 14.54s/it]
+[0;36m(EngineCore_DP0 pid=5136)[0;0m
+[0;36m(EngineCore_DP0 pid=5136)[0;0m
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/70 [00:00<?, ?it/s][0;36m(EngineCore_DP0 pid=5136)[0;0m WARNING 01-16 14:14:26 [utils.py:250] Using default LoRA kernel configs
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   1%|▏         | 1/70 [00:03<04:17,  3.73s/it]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   4%|▍         | 3/70 [00:06<02:02,  1.83s/it]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   7%|▋         | 5/70 [00:06<00:59,  1.09it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  10%|█         | 7/70 [00:06<00:35,  1.80it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  13%|█▎        | 9/70 [00:06<00:22,  2.71it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  16%|█▌        | 11/70 [00:06<00:15,  3.84it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  19%|█▊        | 13/70 [00:06<00:11,  5.17it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  21%|██▏       | 15/70 [00:06<00:08,  6.62it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  24%|██▍       | 17/70 [00:06<00:06,  7.95it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  27%|██▋       | 19/70 [00:07<00:05,  9.42it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  30%|███       | 21/70 [00:07<00:04, 10.76it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  33%|███▎      | 23/70 [00:07<00:03, 11.82it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  36%|███▌      | 25/70 [00:07<00:03, 12.83it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  39%|███▊      | 27/70 [00:07<00:03, 13.71it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  41%|████▏     | 29/70 [00:07<00:02, 14.34it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  44%|████▍     | 31/70 [00:07<00:02, 14.55it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  47%|████▋     | 33/70 [00:07<00:02, 14.87it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  50%|█████     | 35/70 [00:09<00:09,  3.67it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  53%|█████▎    | 37/70 [00:10<00:13,  2.46it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  56%|█████▌    | 39/70 [00:11<00:09,  3.30it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  59%|█████▊    | 41/70 [00:11<00:06,  4.35it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  61%|██████▏   | 43/70 [00:11<00:04,  5.58it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  64%|██████▍   | 45/70 [00:11<00:03,  6.94it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  67%|██████▋   | 47/70 [00:11<00:02,  8.35it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  70%|███████   | 49/70 [00:11<00:02,  9.75it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  73%|███████▎  | 51/70 [00:11<00:01, 10.91it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  76%|███████▌  | 53/70 [00:11<00:01, 12.07it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  79%|███████▊  | 55/70 [00:12<00:01, 13.07it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  81%|████████▏ | 57/70 [00:12<00:00, 13.90it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  84%|████████▍ | 59/70 [00:12<00:00, 14.53it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  87%|████████▋ | 61/70 [00:12<00:00, 14.92it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  90%|█████████ | 63/70 [00:12<00:00, 15.25it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  93%|█████████▎| 65/70 [00:12<00:00, 15.51it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  96%|█████████▌| 67/70 [00:12<00:00, 15.37it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  99%|█████████▊| 69/70 [00:16<00:00,  1.48it/s]
+Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 70/70 [00:17<00:00,  4.11it/s]
+[0;36m(EngineCore_DP0 pid=5136)[0;0m
+Capturing CUDA graphs (decode, FULL):   0%|          | 0/38 [00:00<?, ?it/s]
+Capturing CUDA graphs (decode, FULL):   3%|▎         | 1/38 [00:00<00:05,  7.02it/s]
+Capturing CUDA graphs (decode, FULL):   8%|▊         | 3/38 [00:00<00:02, 11.85it/s]
+Capturing CUDA graphs (decode, FULL):  13%|█▎        | 5/38 [00:00<00:02, 13.39it/s]
+Capturing CUDA graphs (decode, FULL):  18%|█▊        | 7/38 [00:00<00:02, 14.20it/s]
+Capturing CUDA graphs (decode, FULL):  24%|██▎       | 9/38 [00:00<00:01, 14.80it/s]
+Capturing CUDA graphs (decode, FULL):  29%|██▉       | 11/38 [00:00<00:01, 15.04it/s]
+Capturing CUDA graphs (decode, FULL):  34%|███▍      | 13/38 [00:00<00:01, 15.23it/s]
+Capturing CUDA graphs (decode, FULL):  39%|███▉      | 15/38 [00:01<00:01, 15.41it/s]
+Capturing CUDA graphs (decode, FULL):  45%|████▍     | 17/38 [00:01<00:01, 15.27it/s]
+Capturing CUDA graphs (decode, FULL):  50%|█████     | 19/38 [00:01<00:01, 15.39it/s]
+Capturing CUDA graphs (decode, FULL):  55%|█████▌    | 21/38 [00:01<00:01, 15.46it/s]
+Capturing CUDA graphs (decode, FULL):  61%|██████    | 23/38 [00:01<00:00, 15.49it/s]
+Capturing CUDA graphs (decode, FULL):  66%|██████▌   | 25/38 [00:01<00:00, 15.43it/s]
+Capturing CUDA graphs (decode, FULL):  71%|███████   | 27/38 [00:01<00:00, 15.55it/s]
+Capturing CUDA graphs (decode, FULL):  76%|███████▋  | 29/38 [00:01<00:00, 15.67it/s]
+Capturing CUDA graphs (decode, FULL):  82%|████████▏ | 31/38 [00:02<00:00, 15.78it/s]
+Capturing CUDA graphs (decode, FULL):  87%|████████▋ | 33/38 [00:02<00:00, 15.88it/s]
+Capturing CUDA graphs (decode, FULL):  92%|█████████▏| 35/38 [00:02<00:00, 15.92it/s]
+Capturing CUDA graphs (decode, FULL):  97%|█████████▋| 37/38 [00:02<00:00, 15.72it/s]
+Capturing CUDA graphs (decode, FULL): 100%|██████████| 38/38 [00:02<00:00, 15.27it/s]
+  vLLM engine initialized successfully
+  Quantization: none
+  Block size: 16
+Loading LoRA adapter: /workspace/distributed-sft-rl-inference-pipeline/checkpoints/final_model
+  LoRA adapter loaded: validation_adapter
+  LoRA ID: 1
+  LoRA path: /workspace/distributed-sft-rl-inference-pipeline/checkpoints/final_model
+Model loaded successfully!
+============================================================
+Using ConfigurableDataset for aimo3-ref-val-1 (columns mapping detected)
+Loading dataset: vinoku89/aimo3-ref-val (split: validation)
+'(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 3f497b28-17b3-44c5-a471-67ad86d26257)')' thrown while requesting HEAD https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/aimo3-ref-val.py
+[2026-01-16 14:15:09] WARNING _http.py:319: '(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 3f497b28-17b3-44c5-a471-67ad86d26257)')' thrown while requesting HEAD https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/aimo3-ref-val.py
+Retrying in 1s [Retry 1/5].
+[2026-01-16 14:15:09] WARNING _http.py:328: Retrying in 1s [Retry 1/5].
+'(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 5e4e1d28-91c2-43a7-977f-55bf1a0e01ac)')' thrown while requesting HEAD https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/aimo3-ref-val.py
+[2026-01-16 14:15:21] WARNING _http.py:319: '(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 5e4e1d28-91c2-43a7-977f-55bf1a0e01ac)')' thrown while requesting HEAD https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/aimo3-ref-val.py
+Retrying in 2s [Retry 2/5].
+[2026-01-16 14:15:21] WARNING _http.py:328: Retrying in 2s [Retry 2/5].
+'(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 35407032-3486-4cbe-b0c3-3a0185595db9)')' thrown while requesting HEAD https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/.huggingface.yaml
+[2026-01-16 14:15:34] WARNING _http.py:319: '(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 35407032-3486-4cbe-b0c3-3a0185595db9)')' thrown while requesting HEAD https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/.huggingface.yaml
+Retrying in 1s [Retry 1/5].
+[2026-01-16 14:15:34] WARNING _http.py:328: Retrying in 1s [Retry 1/5].
+'(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: e4e2b083-42f3-4f8d-8174-a44625a76848)')' thrown while requesting GET https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/reference_val.csv
+[2026-01-16 14:15:49] WARNING _http.py:319: '(ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: e4e2b083-42f3-4f8d-8174-a44625a76848)')' thrown while requesting GET https://huggingface.co/datasets/vinoku89/aimo3-ref-val/resolve/ce7a50c0adbd74e874c0a145dd6b2f4a26afa6af/reference_val.csv
+Retrying in 1s [Retry 1/5].
+[2026-01-16 14:15:49] WARNING _http.py:328: Retrying in 1s [Retry 1/5].
+Generating validation split:   0%|          | 0/10 [00:00<?, ? examples/s]
+Generating validation split: 100%|██████████| 10/10 [00:00<00:00, 1171.92 examples/s]
+Loaded 1 samples from aimo3-ref-val-1
+Validating on aimo3-ref-val-1 with vLLM...
+  Samples: 1
+  Batch size: 32
+  Metric: accuracy
+============================================================
+SAMPLE VALIDATION PROMPT (first example):
+============================================================
+<|im_start|>system
+You are a helpful and reflective maths assistant. Please reason step by step to put the answer in \boxed{}.<|im_end|>
+<|im_start|>user
+Let $ABC$ be an acute-angled triangle with integer side lengths and $AB<AC$. Points $D$ and $E$ lie on segments $BC$ and $AC$, respectively, such that $AD=AE=AB$. Line $DE$ intersects $AB$ at $X$. Circles $BXD$ and $CED$ intersect for the second time at $Y \neq D$. Suppose that $Y$ lies on line $AD$. There is a unique such triangle with minimal perimeter. This triangle has side lengths $a=BC$, $b=CA$, and $c=AB$. Find the remainder when $abc$ is divided by $10^{5}$.
+Please reason step by step, and put your final answer within \boxed{}.<|im_end|>
+<|im_start|>assistant
+Ground truth: 336
+============================================================
+  Processing batch 1/1...
+Adding requests:   0%|          | 0/1 [00:00<?, ?it/s]WARNING 01-16 14:16:00 [input_processor.py:250] vLLM has deprecated support for supporting different tokenizers for different LoRAs. By default, vLLM uses base model's tokenizer. If you are using a LoRA with its own tokenizer, consider specifying `--tokenizer [lora_path]` to use the LoRA tokenizer.
+Adding requests: 100%|██████████| 1/1 [00:00<00:00, 265.01it/s]
+Processed prompts:   0%|          | 0/1 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
+Processed prompts: 100%|██████████| 1/1 [00:30<00:00, 30.38s/it, est. speed input: 6.35 toks/s, output: 128.46 toks/s]
+Processed prompts: 100%|██████████| 1/1 [00:30<00:00, 30.38s/it, est. speed input: 6.35 toks/s, output: 128.46 toks/s]
+Processed prompts: 100%|██████████| 1/1 [00:30<00:00, 30.38s/it, est. speed input: 6.35 toks/s, output: 128.46 toks/s]
+============================================================
+SAMPLE MODEL RESPONSE (first example):
+============================================================
+<think>
+Okay, so I need to solve this geometry problem. Let me try to parse it step by step.
+We have an acute-angled triangle ABC with integer side lengths, and AB is shorter than AC. Points D and E are on BC and AC respectively, such that AD = AE = AB. Then, line DE intersects AB at X. Circles BXD and CED intersect for the second time at Y ≠ D. It's given that Y lies on line AD, and there's a unique triangle with minimal perimeter satisfying all these conditions. The triangle has sides a = BC, b = AC, c = AB. Find abc mod 10^5.
+First, let me try to visualize the problem. ABC is an acute triangle, so all angles are less than 90 degrees. AB < AC, so triangle is not isosceles with AB = AC. Points D and E are on BC and AC such that AD = AE = AB. So, AD and AE are equal to AB. Since AB is a side, AD and AE are segments from A to points D and E on BC and AC respectively.
+Let me sketch this mentally. Point A is connected to D on BC and E on AC, making AD = AE = AB. Then line DE crosses AB at X. Then circles BXD and CED intersect again at Y ≠ D, and Y lies on AD. The problem states there's a unique minimal perimeter triangle with these properties. So, we need to find the perimeter of that triangle and then compute abc mod 10^5.
+First, maybe I should assign variables to the sides. Let me denote AB = c, AC = b, BC = a. Since AB < AC, c < b. So, sides are a, b, c with c < b, and all positive integers.
+Now, points D and E are on BC and AC such that AD = AE = AB = c. Let me try to find coordinates for points D and E. Maybe coordinate geometry can help here. Let me place point A at the origin (0, 0) for simplicity. Let me assign coordinates to points B and C.
+But since AB = c, and AB is a side, maybe it's easier to place point A at (0, 0), point B at (c, 0), since AB is along the x-axis. Then point C will be somewhere in the plane such that AC = b and BC = a, and the triangle is acute-angled. Wait, but if A is at (0, 0), B is at (c, 0), then coordinates of C can be (d, e)
+... [truncated]
+============================================================
+  Result: 0/1 (0.0%)
+  Time: 30.4s (30394.4ms/query)
+  Throughput: 0.0 queries/sec
+Using ConfigurableDataset for aimo3-syn-val-1 (columns mapping detected)
+Loading dataset: vinoku89/aimo3-syn-val-1 (split: validation)
+Generating validation split:   0%|          | 0/24 [00:00<?, ? examples/s]
+Generating validation split: 100%|██████████| 24/24 [00:00<00:00, 4701.91 examples/s]
+Loaded 1 samples from aimo3-syn-val-1
+Validating on aimo3-syn-val-1 with vLLM...
+  Samples: 1
+  Batch size: 32
+  Metric: accuracy
+============================================================
+SAMPLE VALIDATION PROMPT (first example):
+============================================================
+<|im_start|>system
+You are a helpful and reflective maths assistant. Please reason step by step to put the answer in \boxed{}.<|im_end|>
+<|im_start|>user
+Let $n \\geq 5$ be a positive integer. We call a positive integer $m$ $n$-\\textit{Indonesian} if there exist five positive integers $a_1 \\leq a_2 \\leq a_3 \\leq a_4 \\leq a_5$ such that $a_1 + a_2 + a_3 + a_4 + a_5 = n$ and $a_1 a_2 a_3 a_4 a_5 = m$. Let $f(n)$ denote the smallest $n$-Indonesian positive integer. Let $M = 5^{2025!}$ and for a non-negative integer $c$ define \\[ g(c) = \\frac{1}{2025!} \\left\\lfloor \\frac{2025! f(M+c)}{M} \\right\\rfloor. \\] We can write \\[ g(0) + g(M) + g(2M) + g(3M) + g(4M) + g(5M) = \\frac{p}{q} \\] where $p$ and $q$ are coprime positive integers. What is the remainder when $p+q$ is divided by $99991$?
+Please reason step by step, and put your final answer within \boxed{}.<|im_end|>
+<|im_start|>assistant
+Ground truth: 10052
+============================================================
+  Processing batch 1/1...
+Adding requests:   0%|          | 0/1 [00:00<?, ?it/s]
+Adding requests: 100%|██████████| 1/1 [00:00<00:00, 423.62it/s]
+Processed prompts:   0%|          | 0/1 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
+Processed prompts: 100%|██████████| 1/1 [00:17<00:00, 17.06s/it, est. speed input: 19.52 toks/s, output: 220.59 toks/s]
+Processed prompts: 100%|██████████| 1/1 [00:17<00:00, 17.06s/it, est. speed input: 19.52 toks/s, output: 220.59 toks/s]
+Processed prompts: 100%|██████████| 1/1 [00:17<00:00, 17.06s/it, est. speed input: 19.52 toks/s, output: 220.59 toks/s]
+============================================================
+SAMPLE MODEL RESPONSE (first example):
+============================================================
+<think>
+Okay, let's try to tackle this problem step by step. First, I need to understand what exactly is being asked here. The problem involves some definitions related to numbers and their properties, specifically about n-Indonesian numbers and then computing a sum involving g(c) values.
+Starting with the definition:
+- A positive integer m is n-Indonesian if there exist five positive integers a1 ≤ a2 ≤ a3 ≤ a4 ≤ a5 such that their sum is n and their product is m.
+- f(n) is the smallest n-Indonesian positive integer.
+Then there's a function g(c) defined as 1/(2025!) times the floor of (2025! * f(M + c))/M, where M = 5^{2025!}, and c is a non-negative integer.
+The problem asks to compute g(0) + g(M) + g(2M) + g(3M) + g(4M) + g(5M), which equals p/q, and then find p + q modulo 99991.
+First, let me try to figure out what f(n) is. Since m is an n-Indonesian number, and we need the smallest such m. The question is about the minimal m for each n.
+But before that, let's think about the definition of an n-Indonesian number. For a given n, we need five numbers whose sum is n and product is m. The minimal m would be determined by the way we can split the numbers into five parts that are as small as possible, but their product is as small as possible.
+Wait, but since the numbers are ordered, maybe there's a way to minimize the product given a fixed sum. For example, if we want to minimize the product of five numbers with a fixed sum, we should use as equal as possible values. Because, for fixed sum, the product is maximized when the numbers are as equal as possible. Therefore, if we need to minimize the product, maybe we need to make the numbers as unequal as possible? Wait, but if the numbers are unequal, their product might be smaller? Or maybe not?
+Wait, actually, for fixed sum, the product is maximized when numbers are equal. Therefore, if we have to minimize the product, perhaps we need to make some numbers as small as possible and others as large as possible? Bu
+... [truncated]
+============================================================
+  Result: 0/1 (0.0%)
+  Time: 17.1s (17063.9ms/query)
+  Throughput: 0.1 queries/sec
+Uploading validation results to vinoku89/aimo3-exp-1 (branch: qwen3-0.6b-sft-lora-20260116_220920-004)...
+Uploaded logs/validation_results_20260116_220920.json to vinoku89/aimo3-exp-1 (main branch)
+wandb: uploading artifact run-jo6acl31-validation_summary; updating run metadata
+wandb: uploading artifact run-jo6acl31-validation_summary; uploading output.log; uploading wandb-summary.json
+wandb: uploading artifact run-jo6acl31-validation_summary; uploading output.log; uploading config.yaml
+wandb: uploading artifact run-jo6acl31-validation_summary
+wandb: uploading history steps 0-3, summary, console lines 126-159
+wandb:
+wandb: Run history:
+wandb:     aimo3-ref-val-1/accuracy ▁
+wandb:      aimo3-ref-val-1/correct ▁
+wandb: aimo3-ref-val-1/ms_per_query ▁
+wandb: aimo3-ref-val-1/time_seconds ▁
+wandb:        aimo3-ref-val-1/total ▁
+wandb:     aimo3-syn-val-1/accuracy ▁
+wandb:      aimo3-syn-val-1/correct ▁
+wandb: aimo3-syn-val-1/ms_per_query ▁
+wandb: aimo3-syn-val-1/time_seconds ▁
+wandb:        aimo3-syn-val-1/total ▁
+wandb:                           +4 ...
+wandb:
+wandb: Run summary:
+wandb:     aimo3-ref-val-1/accuracy 0
+wandb:      aimo3-ref-val-1/correct 0
+wandb: aimo3-ref-val-1/ms_per_query 30394.41
+wandb:        aimo3-ref-val-1/score 0/1
+wandb: aimo3-ref-val-1/time_seconds 30.39
+wandb:        aimo3-ref-val-1/total 1
+wandb:     aimo3-syn-val-1/accuracy 0
+wandb:      aimo3-syn-val-1/correct 0
+wandb: aimo3-syn-val-1/ms_per_query 17063.86
+wandb:        aimo3-syn-val-1/score 0/1
+wandb:                          +10 ...
+wandb:
+wandb: 🚀 View run qwen3-0.6b-sft-lora-20260116_220920-004 at: https://wandb.ai/vinokuteam/aimo3-exp-1-val/runs/jo6acl31
+wandb: ⭐️ View project at: https://wandb.ai/vinokuteam/aimo3-exp-1-val
+wandb: Synced 5 W&B file(s), 1 media file(s), 2 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20260116_141237-jo6acl31/logs
+============================================================
+VALIDATION SUMMARY
+============================================================
+Model: Qwen/Qwen3-0.6B
+Adapter: /workspace/distributed-sft-rl-inference-pipeline/checkpoints/final_model
+------------------------------------------------------------
+  aimo3-ref-val-1: 0/1 (0.0%)
+  aimo3-syn-val-1: 0/1 (0.0%)
+------------------------------------------------------------
+  Overall: 0/2 (0.0%)
+============================================================
+[rank0]:[W116 14:17:03.283738066 ProcessGroupNCCL.cpp:1524] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[rank0]:[W116 14:17:04.387464649 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[W116 14:17:06.876855411 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())