Lambent/Qwen3-4B-Base-Continued-GRPO-Style-Karcher

For this one...

... (over)trained a SmolLM2-360M on 5 epochs at swept-for LR and rank on each of the target domains to fit style, then rewarded the model for lowering perplexity on the proxy model.

In this case, trained an adapter per domain and then Karcher merged them. I'm not sure if any of the domains had notably different effect, they all basically had the same result on evals. However, the karcher combination of them seem to have significantly lowered perplexity on lambada_openai, which is interesting enough to publish.

Additionally, attempted to implement MARA from https://im-ant.github.io/mara/ on the GRPO side to help preserve distribution entropy, though I'm unsure how correctly/usefully we did so.

Task	Metric	Qwen3-4B-Base	GRPO-Merge	Δ Base	GRPO-Wave	Δ Base	Δ Merge	Style-Karcher	Δ Base	Δ Wave
arc_easy	acc	0.7891	0.7870	-0.27%	0.7912	+0.27%	+0.53%	0.7883	-0.10%	-0.37%
arc_easy	acc_norm	0.7609	0.7605	-0.05%	0.7643	+0.45%	+0.50%	0.7576	-0.43%	-1.04%
lambada_openai	acc	0.6912	0.6984	+1.04%	0.7006	+1.36%	+0.31%	0.7087	+2.53%	+1.16%
lambada_openai	perplexity ↓	4.2433	4.0490	-4.58%	3.9616	-6.64%	-2.16%	3.8343	-9.63%	-3.21%
openbookqa	acc	0.3160	0.3180	+0.63%	0.3180	+0.63%	±0.00%	0.3160	±0.00%	-0.63%
openbookqa	acc_norm	0.4100	0.4120	+0.49%	0.4100	±0.00%	-0.49%	0.4080	-0.49%	-0.49%
piqa	acc	0.7797	0.7807	+0.13%	0.7813	+0.21%	+0.08%	0.7786	-0.14%	-0.35%
piqa	acc_norm	0.7807	0.7807	±0.00%	0.7813	+0.08%	+0.08%	0.7807	±0.00%	-0.08%

Some very interesting results on diversity also:

Diversity Metrics (Qwen3-4B-Base vs Style-Karcher, temperature=1.0, 8 completions per prompt)

Domain	Metric	Base	Karcher	Δ
ao3_english	Prefix entropy	3.309	3.238	-2.1%
ao3_english	Distinct-1	0.618	0.683	+10.5%
ao3_english	Distinct-2	0.962	0.984	+2.3%
ao3_english	Pairwise diversity	0.919	0.932	+1.4%
github_python	Prefix entropy	1.514	1.456	-3.8%
github_python	Distinct-1	0.610	0.624	+2.3%
github_python	Distinct-2	0.890	0.876	-1.6%
github_python	Pairwise diversity	0.933	0.933	±0.0%
wikipedia_english	Prefix entropy	1.974	1.892	-4.2%
wikipedia_english	Distinct-1	0.599	0.559	-6.7%
wikipedia_english	Distinct-2	0.932	0.898	-3.6%
wikipedia_english	Pairwise diversity	0.907	0.900	-0.8%
bbc_news	Prefix entropy	2.252	2.186	-2.9%
bbc_news	Distinct-1	0.557	0.577	+3.6%
bbc_news	Distinct-2	0.949	0.951	+0.3%
bbc_news	Pairwise diversity	0.901	0.908	+0.8%
arxiv_cs	Prefix entropy	2.455	2.346	-4.4%
arxiv_cs	Distinct-1	0.555	0.567	+2.3%
arxiv_cs	Distinct-2	0.905	0.906	+0.2%
arxiv_cs	Pairwise diversity	0.895	0.901	+0.7%

Additional experiment (after quantization, should affect further training but not existing quants): Initializing the <think></think> tokens in embedding space.

Original embeddings were identical (cos=1.0) at 0.3x norm, untrained.

Optimized via AdamW on GSM8k reasoning traces with 3-shot prefix, loss on reasoning+answer tokens, norm clamped to 1.5x avg embedding norm.

After: two distinct vectors (cos=0.07) at 1.5x norm. GSM8k 3-shot accuracy: 96.7% (29/30) vs 90.0% with original embeddings. CE loss improvement: +7.8% on held-out eval.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Karcher Mean merge method.

Models Merged

The following models were included in the merge:

• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-github_javascript-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-arxiv_cs-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-general-ao3style-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-ao3_english-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-arxiv_math-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-github_python-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-wikipedia_english-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-arxiv_physics-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-github_cpp-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-bbc_news-mara-360m
• ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave + ../rlvr-envs/grpo-github_markdown-mara-360m

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-ao3_english-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-arxiv_cs-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-arxiv_math-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-arxiv_physics-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-bbc_news-mara-360m

  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-github_cpp-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-github_javascript-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-github_markdown-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-github_python-mara-360m
  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-wikipedia_english-mara-360m

  - model: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave+../rlvr-envs/grpo-general-ao3style-360m
merge_method: karcher
dtype: bfloat16
tokenizer_source: ../rlvr-envs/Qwen3-4B-Base-Continued-GRPO-Wave