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qwen3_30b_a3b_to_4b_onpolicy_science_5k

README

License: apache-2.0

Models

Table with columns: Role, Model
Role	Model
Student	RockToken/qwen3_30b_a3b_to_4b_offpolicy_20k
Teacher	Qwen/Qwen3-30B-A3B-Instruct-2507 (MoE)

enable_thinking=False throughout.

Training data

Source: open-thoughts/OpenThoughts3-1.2M, domain == "science" slice
5,000 single-user-turn prompts; 69 exceeding prompt_max_len=1280 were filtered, leaving 4,931
Only the prompts are used; on-policy KD never reads the dataset's reference answers

Training setup

Framework: KDFlow — FSDP2 + SGLang rollout, Ray-orchestrated GPU co-location with sleep/wakeup. Same pipeline revision as the math-domain onpolicy_5k_src* runs.

Hardware: 1× node, 4× H100 (94 GB), 24 h 02 min wall-clock (~96 GPU-hours).

Key hyperparameters

Table with columns: Group, Value
Group	Value
Backend	fsdp2, bf16, gradient ckpt on
Epochs	1 (2,465 rollout iterations)
Train batch	4 (micro 1)
Learning rate	2e-6, cosine, warmup 5%
KD ratio	1.0
KD loss	reverse KL (`rkl`)
KD algorithm	`vanilla_kd`
Rollout engine

prompt_max_len is 1280 instead of the math runs' 800: science prompts run longer (p99 ≈ 1,182 tokens).

Note on the LR schedule: as in the earlier runs of this series, the scheduler's horizon is sized to 4× the actual number of updates, so the cosine decay is only partially traversed — the LR moves from 2.00e-6 to 1.79e-6 over the run rather than reaching zero. Kept as is for comparability within the series.

Training dynamics

Means over 500-step blocks (per-step values are noisy at batch 4):

Table with columns: steps, loss (reverse KL), mean gen length
steps	loss (reverse KL)	mean gen length
1–500	0.273	3,250
501–1000	0.256	3,417
1001–1500	0.253	3,456
1501–2000	0.248	3,390
2001–2465	0.242	3,321

The loss starts near 0.27 rather than the ~3 seen when distilling from a raw instruct model — the student has already been through off-policy KD against the same teacher, so on-policy training starts close and grinds out the remaining distribution gap.

Generation lengths: p50 = 3,267, p95 = 6,456, max = 8,000 against the 8,000 cap. A small tail of responses hits the cap; the bulk is untruncated.

Intended use

Research on distillation dynamics, in particular whether on-policy KD gains transfer across domains (math-trained off-policy base → science on-policy round). Domain: science (OpenThoughts-3 science split).

Limitations

The on-policy round is science-only; not tuned for chat or safety.
enable_thinking=False — this student does not emit thinking traces.
A small fraction of rollouts was truncated at 8,000 tokens during training.
Single checkpoint at the end of one epoch; no intermediate checkpoints were kept.

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Model Details

Model Provider

RockToken

Model Tree

Base

RockToken/qwen3_30b_a3b_to_4b_offpolicy_20k

Fine-tuned

this model

Input Modalities

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Output Modalities