littlelearner / littlelearner-1.3b-grpo-math-expert

littlelearner-1.3b-grpo-math-expert

1.36B K-5-bounded chat model post-trained with GRPO on top of SFT.

Part of the LittleLearner scale-up study ( pedagogically-controlled knowledge exposure ): Qwen3 dense LMs trained on a corpus filtered to U.S. K-5 material ( bounded ) vs an unfiltered FineWeb-Edu corpus ( unbounded ), to measure what an interpretable knowledge boundary costs and grants.

Note: This checkpoint was post-trained with GRPO on mathematical reasoning tasks to probe achievable performance on MathCAMPS. As a result, its behavior is specialized toward mathematical reasoning and may not preserve general-purpose chat capabilities; responses may also exhibit a tendency toward math-oriented reasoning or output.

Model

• Architecture: Qwen3 dense ( Qwen3ForCausalLM ).
• Size: 1.358B params, hidden 2048, 26 layers, 16 query / 8 KV heads, FFN 5632. Context: 4096.
• Tokenizer: custom 64k byte-level BPE with per-digit splitting (ChatML special tokens).
• Pretraining: 88B tokens on K-5 LittleCurriculum (FineWeb-Edu filtered to U.S. grades K-5). WSD schedule, sharded Muon, MXFP8, Megatron-Core on 8xB200.
• SFT: supervised fine-tuned on K-5 chat data (lr 1e-5, 3 epochs) from a cooloff-SFT-primed base.
• RL (GRPO): segmented policy re-banding on a strictly K-5 verifiable-answer pool (Gemini-generated K-5 word problems + K-5-filtered GSM8K): 4 segments at rollout/training temperature 1.0, then 2 more at temperature 2.0 (the bounded-corpus, 1.3B-scale unlock temperature).

Evaluation

MathCAMPS:

• K-5 pass@64 61.2 / pass@1 40.1
• beyond-K-5 pass@64 17.1 / pass@1 6.5

Usage

# transformers (chat)
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = "manueldeprada/littlelearner-1.3b-bounded-grpo"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="cuda")
msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

# vLLM
from vllm import LLM
repo = "manueldeprada/littlelearner-1.3b-bounded-grpo"
llm = LLM(repo)
msgs = [{"role": "user", "content": "Liam has 3 apples and buys 4 more. How many apples does he have?"}]
print(llm.chat(msgs)[0].outputs[0].text)