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efficient-moe-agent-project/
scienceworld

Name: scienceworld
Availability: InStock

peft

safetensors

reinforcement-learning

Qwen/Qwen2.5-1.5B-Instruct

adapter

apache-2.0

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ScienceWorld — single-expert LoRA (GiGPO RL, gold-SFT warm start)

LoRA adapters for Qwen/Qwen2.5-1.5B-Instruct, trained as a single-expert agent on the ScienceWorld text environment (30 elementary-science tasks) with GiGPO reinforcement learning, warm-started from behaviour cloning on gold paths.

Run: scienceworld_single_warmstart_ms100_1k_seed0 (seed 0).

Checkpoints

folder	step	val test_score	val success_rate	note
`single_expert_warmstart/final_step1000/`	1000	5.11	0.271	final policy (1000 RL steps)
`single_expert_warmstart/best_step140/`	140	5.50	0.314	best checkpoint by `val/success_rate`

val/text/test_score is the mean ScienceWorld raw score / 10 over a fixed, stratified dev validation set (140 episodes, all 30 task types, greedy decode). So test_score ≈ 5.1 means an average raw score of ~51/100; success_rate is the fraction of episodes fully solved (raw score = 100).

Note: intermediate checkpoints (e.g. the test_score peaks at steps 210 / 510, both ~5.6–5.7) were not retained; only the final and the best-by-success-rate checkpoints are available.

Training setup

Base model: Qwen/Qwen2.5-1.5B-Instruct
Adapter: LoRA, r = 64, α = 64, target = all-linear (q,k,v,o,gate,up,down)
Warm start: gold-path behaviour-cloning SFT adapter (replay of env.getGoldActionSequence(); ~6k (prompt, <think>…</think><action>…</action>) pairs; 3 epochs). Cold-start RL never solves a task (val success ≈ 0), so the warm start is required to give GiGPO a learning signal.
RL: GiGPO (adv_estimator=gigpo, γ = 0.95), lr = 3e-6, 1000 steps.
- train_batch_size = 8, GiGPO group rollout.n = 8, ppo_mini_batch_size = 64
- invalid-action penalty coef = 0.1
Env: ScienceWorld, max_steps = 100, history_length = 2 (last 2 obs+action pairs in the prompt). Reward = per-step ScienceWorld score delta / 10 (dense).
Validation: fixed stratified dev set (140 episodes, 30 tasks × ~5 variations), greedy (temperature 0), every 10 steps.

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained(base)
model = PeftModel.from_pretrained(
    model,
    "efficient-moe-agent-project/scienceworld",
    subfolder="single_expert_warmstart/best_step140",  # or final_step1000
)