README.md

metadata

pretty_name: res-vllm

ryzax/res-vllm

Copy of ryzax/res with vLLM-tool AIME25 runs swapped into the SUMM and LASTK folders. Original ryzax/res is unchanged.

All swapped/added runs are Qwen3-1.7B, AIME25 (30 problems × 64 samples), window 4096, max gen 262144, BUDGET_FORCE_SAVING=256 for summary variants.

Swaps

Path	What it is now	AIME25 avg@64	cov@64	maj@64
Qwen3-1.7B-SUMM/w4096s256	force256 + SAVING_PROMPT=context_system (evaluate-context example on last save only)	26.5%	56.7%	40.0%
Qwen3-1.7B-LASTK/w4096k256	last-k 256 with subtract	23.8%	50.0%	36.7%

Previously these folders held the Hugging Face Transformers SUMM (w4096s256) and LASTK (w4096k256) runs from Oct 2025.

SUMM ablations

Path	Prompt	avg@64	cov@64	maj@64
Qwen3-1.7B-SUMM/w4096s256context	force256 + context	26.4%	53.3%	36.7%
Qwen3-1.7B-SUMM/w4096s256context_maybe	force256 + context_maybe	25.8%	46.7%	40.0%
Qwen3-1.7B-SUMM/w4096s256contextnoshot	force256 + context, no few-shot example in the system prompt	23.2%	33.3%	33.3%
Qwen3-1.7B-SUMM/w4096s256context_system	force256 + context_system, evaluate-context example on every save	22.8%	36.7%	33.3%

LASTK ablations

Path	Setting	avg@64	cov@64	maj@64
Qwen3-1.7B-LASTK/w4096k256nosubtract	last-k 256, no subtract	26.3%	53.3%	43.3%

Dense / sliding-window folders are the same files as ryzax/res.

Length metrics and budget-clipped avg

results_*.json scored only the last assistant window (~3k tokens), so tok@ is too small and avg@ jumps by 4k. Use:

• results_*.tokfixed.json — full-trace tok@ / too_long@ from tool_usage.generated_tokens (accuracy unchanged)
• results_concatted_tok262144.json — aime/utils.py rerun on concatenated saving windows + final (the old completions_to_return_concatted path). Full-budget avg matches the original; mid-budget avg rises with true length.

Path	tok@262144@64	avg@4k@64	avg@32k@64	avg@262k@64
Qwen3-1.7B-SUMM/w4096s256	123615	14.8%	22.3%	26.5%
Qwen3-1.7B-SUMM/w4096s256context	122880	15.5%	22.4%	26.4%
Qwen3-1.7B-SUMM/w4096s256context_maybe	139369	15.6%	22.9%	25.8%
Qwen3-1.7B-SUMM/w4096s256contextnoshot	153125	4.7%	19.2%	23.2%
Qwen3-1.7B-LASTK/w4096k256	129723	3.5%	20.1%	23.9%
Qwen3-1.7B-LASTK/w4096k256nosubtract	128707	3.2%	20.4%	26.3%
Qwen3-1.7B-SUMM/w4096s256context_system	119720	15.7%	20.8%	22.8%

w4096s256context_system scored concatenated windows live (results_*.json already has full-trace tok@ and rising mid-budget avg), so it has no tokfixed / results_concatted sidecars. Applying the evaluate-context example on every save hurts vs last-save-only w4096s256 (22.8% vs 26.5% avg@64).