Add refill strategy Implementation and some evaluation results

[tengxiao1]

This PR implements the refill strategy in the training pipeline and documents the resulting performance improvements.

The refill strategy fills incomplete training batches with unfiltered prompts to maintain batch size and maximize data usage efficiency.

The comparison below is based on the same 3000 training steps on GRPO.

Benchmark	Before Refill	After Refill	Δ (Change)
BBH	65.20	67.20	+2.00
MMLU	73.38	76.05	+2.67
PopQA	21.16	20.64	-0.52
ifeval	83.73	85.95	+2.22
GPQA	33.92	33.49	-0.43
AIME	7.78	11.11	+3.33
ZebraLogic	9.80	10.20	+0.40
MATH500	75.20	76.60	+1.40
GSM8K	88.02	89.84	+1.82
MATH	73.14	74.00	+0.86
AGI (average)	—	68.55	—