Checkpoints
Table with columns: Checkpoint, Source step, Validation avg@16, best@16 / pass@16, maj@16| Checkpoint | Source step | Validation avg@16 | best@16 / pass@16 | maj@16 |
|---|
| Root final | 100 | 0.700000 | 0.768713 | 0.718600 |
best_avg16/ | 60 | 0.731250 | 0.816338 | 0.740938 |
Training Run
qwen3gen-physics-RLSD-Qwen-Qwen3-4B-mbs8-decay0-ema0.05-train256-rollout8-lr1e-6-vllm0.8
Base Model
- Base model:
Qwen/Qwen3-4B
- Fine-tuning type: full-parameter FSDP RL training
- Dataset:
datasets/sciknoweval/physics
- Train split: 720 examples
- Validation split: 80 examples
Method
- Method: RLSD
- Config:
rlsd
- Policy loss mode:
rlsd
- Reward: local SciKnowEval multiple-choice reward checker
- Rollout correction: token-level importance sampling, threshold 2.0
Hyperparameters
Table with columns: Field, Value| Field | Value |
|---|
| Base model | Qwen/Qwen3-4B |
| Training steps | 100 |
| Train batch size | 256 |
| Rollouts per prompt | 8 |
| Generations per step | 2048 |
| PPO mini batch size | 8 |
| Learning rate | 1e-6 |
| LR warmup steps | 10 |
Metrics
Table with columns: Metric, Value| Metric | Value |
|---|
| Final training step | 100 |
Final critic/score/mean | 0.890137 |
Final critic/rewards/mean | 0.890137 |
Final validation avg@16 | 0.700000 |
Peak validation avg@16 | 0.731250 |
| Peak validation step | 60 |
Loading
Root final checkpoint:
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-4B-Physics-RLSD")
tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-4B-Physics-RLSD")
Best avg@16 checkpoint:
model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-4B-Physics-RLSD", subfolder="best_avg16")
tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-4B-Physics-RLSD", subfolder="best_avg16")
Intended Use
This model is intended for research on RL fine-tuning and self-distillation behavior on science/generalization tasks. It has not been broadly safety evaluated for production use.
Limitations
The reported scores are training-time and validation-time metrics from the local experimental setup. They should not be interpreted as broad benchmark results without independent evaluation.
