Datasets:

penfever
/

nemotron-gym-competitive-coding-qwen3.5-122b-131k-opencode-traces

conversations listlengths 1 69	agent stringclasses 1 value	model stringclasses 1 value	model_provider stringclasses 1 value	date stringlengths 27 27	task stringlengths 31 31	episode stringclasses 27 values	run_id stringclasses 1 value	trial_name stringlengths 40 40	verifier_output stringlengths 11 25.6k
[ { "role": "assistant", "content": "<think>The user wants me to solve a competitive programming problem. Let me understand the problem:\n\n1. There are n stormtroopers at various (x, y) coordinates\n2. Han Solo's gun is at (x0, y0)\n3. In one shot, he can destroy all stormtroopers on a line that passes throu...	opencode	vllm/7144641945676850	vllm	2026-07-31T11:51:29.192577Z	comp-coding-00035cfe25d9.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-00035cfe25d9.tar.gz__GcwSDfB	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>This is a competitive programming problem. Let me understand it:\n\n1. Given a positive integer x (1 ≤ x ≤ 10^18)\n2. Find the positive integer ≤ x that has the maximum sum of digits\n3. If there are multiple, choose the biggest one\n\nLet me think about the approac...	opencode	vllm/7144641945676850	vllm	2026-08-03T01:11:03.341239Z	comp-coding-000a5333b32f.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-000a5333b32f.tar.gz__7WBBkKr	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>Let me understand the problem:\n\n1. Take a 5-digit number as input\n2. Shuffle the digits: position 1, 3, 5, 4, 2 (1-indexed)\n - For 12345: digits are [1, 2, 3, 4, 5]\n - New order: [1, 3, 5, 4, 2] = 13542\n3. Raise this shuffled number to the 5th power\n4. Ta...	opencode	vllm/7144641945676850	vllm	2026-08-02T19:24:04.708264Z	comp-coding-000daaf57107.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-000daaf57107.tar.gz__YDpq9md	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>Let me analyze this problem step by step.\n\nWe have an n×n matrix with lowercase letters. We can change at most k letters. We need to find the lexicographically smallest string from a path going from (0,0) to (n-1,n-1) moving only right or down.\n\nKey observations...	opencode	vllm/7144641945676850	vllm	2026-08-02T11:56:21.266631Z	comp-coding-00142815e083.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-00142815e083.tar.gz__GAdsYKa	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>The user wants me to solve a competitive programming problem. Let me understand the problem:\n\n1. Given integer n, find the length of n! (factorial of n) excluding trailing zeros.\n\nWait, let me re-read. \"find length of n! excluding trailing zeros\" - this is a b...	opencode	vllm/7144641945676850	vllm	2026-08-01T15:04:59.793978Z	comp-coding-0016b3789925.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-0016b3789925.tar.gz__Nf9JeZZ	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>Let me understand this problem step by step.\n\nWe have n computers, and for each pair (i, j) with i < j, we have a difficulty value a_{i,j}. All these values are distinct.\n\nWe need to partition the computers into k sets A_1, ..., A_k such that:\n1. Each computer ...	opencode	vllm/7144641945676850	vllm	2026-08-01T09:26:46.541719Z	comp-coding-0018e7ea5c91.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-0018e7ea5c91.tar.gz__YWjsEkc	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>Let me analyze this problem step by step.\n\nKevin has an array and wants to make all neighboring elements different by reversing subarrays. We need to find the minimum number of reversals needed.\n\nKey observations:\n1. Reversing a subarray doesn't change the mult...	opencode	vllm/7144641945676850	vllm	2026-08-02T07:35:04.465461Z	comp-coding-00199b267655.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-00199b267655.tar.gz__qAaEPKX	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>Let me understand the problem:\n\n1. A train departs at times 4^k for k ≥ 0, i.e., at times 1, 4, 16, 64, 256, ...\n2. Given a time s (in binary representation), I need to count how many trains departed strictly before time s.\n\nSo I need to find the number of inte...	opencode	vllm/7144641945676850	vllm	2026-08-01T00:46:01.950163Z	comp-coding-0023424f1882.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-0023424f1882.tar.gz__omfMjMd	/app/solution.py missing — agent did not write solution
[ { "role": "assistant", "content": "<think>Let me understand this problem step by step.\n\nWe need to find all possible numbers of teams k such that the tournament results in exactly n games.\n\nLet's analyze the tournament format:\n1. While the number of teams is even, they split in pairs and play. Each sta...	opencode	vllm/7144641945676850	vllm	2026-07-31T14:48:17.722935Z	comp-coding-0025e656af81.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-0025e656af81.tar.gz__4fZWh9v	/app/solution.py missing — agent did not write solution
[{"role":"assistant","content":"<think>Let me understand this problem step by step.\n\nWe need to co(...TRUNCATED)	opencode	vllm/7144641945676850	vllm	2026-08-01T00:15:07.541806Z	comp-coding-0026dbc30856.tar.gz	episode-0	5ddb9cdb-0026-43ee-a822-14893fb9d566	comp-coding-0026dbc30856.tar.gz__Dejq9j2	/app/solution.py missing — agent did not write solution

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Agent trace dataset

Decoding the literal token IDs

The prompt_token_ids / completion_token_ids / logprobs columns are the verbatim tokens the serving engine emitted, stored PER AGENT STEP as a list-of-lists (one inner list per turn). To turn them back into text you MUST use the exact tokenizer the model was served with — a generic same-family tokenizer will decode word tokens to garbage.

Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8

from transformers import AutoTokenizer
# Use the served model's own tokenizer (pull from the ref above; if it is a
# gs:// mirror, copy tokenizer.json/tokenizer_config.json/vocab.json/merges.txt
# locally first and point AutoTokenizer at that dir).
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-122B-A10B-FP8")
# token_ids are list-of-lists (one list per turn) — decode each turn:
text = [tok.decode(turn, skip_special_tokens=False) for turn in completion_token_ids]

Engine-reported served model name: 7144641945676850

See tokenizer_provenance.json for a machine-readable version.

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