jason-cse151b-grpo-lora

CSE 151B SP26 Math Reasoning — GRPO LoRA adapter (r=32, step-606 best)

Stage-2 GRPO LoRA, trained on top of the SFT-merged base.

This is the best-by-val_225 checkpoint (step-606) selected from a 27-checkpoint sweep.

NOTE: base_model_name_or_path points to JaasonYuu/jason-cse151b-model, which is the fully merged SFT+GRPO model — applying this adapter on top of that would double-apply the GRPO delta. The TRUE base of this adapter is the SFT-merged BF16 model (Qwen3-4B-Thinking + SFT LoRA merged). To reproduce that base, apply JaasonYuu/jason-cse151b-sft-lora to Qwen/Qwen3-4B-Thinking-2507 and merge.

Hyperparameters

• LoRA r = 32, alpha = 64, dropout = 0.05
• target_modules = [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
• 3 epochs (606 steps), LR 1e-5 constant_with_warmup (5%)
• max_completion_length = 10240, beta (KL) = 0.04
• num_generations K = 4, hard-pool duplication = 1× (effective K=8 on 100 hard prompts)
• Loss: dr_grpo, importance_sampling_level = sequence, scale_rewards = none
• Reward: course Judger binary + length penalty (MCQ exempt)

val_225 accuracy

Applied on SFT-merged base: 66.22 % (+1.78 pp over SFT alone, +2.22 pp over base Qwen3-4B-Thinking-2507 with starter prompts).

Usage (after reconstructing SFT-merged base)

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Step 1: reconstruct SFT-merged base
base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-4B-Thinking-2507", dtype=torch.bfloat16, device_map="auto",
    trust_remote_code=True,
)
sft = PeftModel.from_pretrained(base, "JaasonYuu/jason-cse151b-sft-lora")
sft_merged = sft.merge_and_unload()

# Step 2: apply GRPO LoRA on top
model = PeftModel.from_pretrained(sft_merged, "JaasonYuu/jason-cse151b-grpo-lora")

OR just use the pre-merged SFT+GRPO model.

See also

• Pre-merged SFT+GRPO BF16: JaasonYuu/jason-cse151b-model
• SFT LoRA: JaasonYuu/jason-cse151b-sft-lora