slippedJim/ATOM_regen_nv_OpenCodeInstruct

ATOM regen — OpenCodeInstruct, answered by Kimi-K3

Prompts from nvidia/OpenCodeInstruct with every answer regenerated by Kimi-K3 itself, at a 32768 window, for off-policy speculative-decoding distillation.

227,329 rows, single-turn.

Composition

Single source, single source value (oci). Selection:

step	rows
rendered prompts, upstream	4,825,044
of those, tier-0 (upstream unit tests all pass)	1,553,642
sampled	1,000,000
generated and verified	244,695
kept	227,329

Only tier-0 was used. Rows whose upstream average_test_score is perfect are the low-entropy, template-shaped workload a speculator accepts best, which is what this data is for.

	mean	p50	p90	p99	max
prompt tokens	303.4	285	416	601	1,702
answer tokens	3,231.5	1,450	8,233	24,452	32,461

734,612,605 answer tokens in total. 9.8% of rows exceed 8192 tokens, 3.5% exceed 16384.

Why code

In our own corpus, code deduped down to 0.43% of the mix and SWE-bench Pro was the second-weakest benchmark at 84.9% of the reference draft. Raising code to 20% — mostly with this data — took it to 100.6%, the largest relative gain of the thirteen benchmarks we track.

Short prompts with long answers also make this a cheap source of long-context positions compared with regenerating long prompts.

Format

{"conversations": [
  {"role": "user", "content": "..."},
  {"role": "assistant", "reasoning_content": "...", "content": "..."}
], "source": "oci"}

reasoning_content holds Kimi-K3's thinking channel, stored separately from content. Multi-turn rows keep the full assistant reasoning of earlier turns: K3 is trained that way, and stripping historical thinking leaves empty think blocks that do not match the context seen at training time.

Generation

Teacher	moonshotai/Kimi-K3, revision a590ce090cb049c93a33dfe8c208ec652aa20503
Engine	ATOM 0.1.6rc1.dev275+g83e71001e (commit 83e71001e94602ee8e1d04581809bded49c3f08b)
Image	rocm/atom-dev:nightly_202608141640
Image digest	sha256:2f8bd4206ad15d014ae48115eae1ee9f1db83781848a8542de7177cfbd4ac914
torch / HIP	2.13.0+rocm7.14.0 / 7.14.60850
Hardware	8 x AMD Instinct MI355X per node, tensor_parallel_size=8
Sampling	greedy (temperature=0), one sample per prompt
KV cache	fp8 (matches the serving stack this data is distilled for)
Speculative decoding	off

Verification

Every row was re-rendered and re-tokenized, then compared token-for-token against what the engine actually consumed and produced. Non-matching rows were dropped (7.10%), led by unclosed thinking blocks.

Limitations

• Single-turn only.
• tier-0 only, so the distribution skews template-shaped. Harder workloads would need tier-1.
• Prompts are short (p50 285 tokens); the long-context value is in the answers, not the prompts.