Scaling the training of Large Language Model (LLM) agents typically requires a large collection of executable environments, whose construction, maintenance, and operation constrain the expansion of training experience. Existing offline trajectories offer an opportunity to reuse interaction experience, yet effectively refining the current policy without revisiting these environments remains challenging: supervised fine-tuning (SFT) can lead to catastrophic forgetting, while online reinforcement learning requires real-time interaction with the environment. To address this challenge, we propose Step-Advantage Policy Distillation (SAPD), a method for learning from offline trajectories using relative step advantages. SAPD decomposes multi-turn trajectories into individual decision contexts for on-policy distillation, allowing the student to generate responses conditioned on recorded histories, and introduces relative step advantage signals to stabilize training. The entire process requires neither new environment observations nor additional multi-turn interactions. Experiments on ALFWorld, WebShop, and Search-based QA show that SAPD improves performance across different teacher configurations, mitigates catastrophic forgetting compared with SFT, and runs at least 4.7× faster per training step than online OPD. We will release our code soon.
Figures & tables
Figure 1: Overview of our environment-free policy refinement framework.
ALFWorld
Search-based QA
WebShop
Method
Pick
Look
Clean
Heat
Cool
Pick2
Avg.
NQ
Triv
Pop
Hotp
2Wk
MuS
Bam
Avg.
Success
SFT
100.0
100.0
94.1
73.7
88.2
84.2
90.6
32.8
47.4
38.3
29.9
36.5
11.1
28.0
37.0
51.6
GRPO
85.3
53.7
84.5
78.2
59.7
53.5
72.8
34.1
54.5
37.8
32.4
31.9
10.3
26.4
37.7
56.8
GRPO teacher model: Qwen2.5-3B-Instruct
GRPO
96.8
88.2
92.9
91.7
90.0
70.0
89.1
43.4
60.8
46.2
39.3
35.3
14.8
66.9
44.5
64.8
OPD
83.4
60.7
72.7
49.1
42.0
13.3
57.0
11.1
25.6
16.9
8.6
15.4
1.7
12.8
16.1
29.9
Table 1: Main results on ALFWorld, Search-based QA, and WebShop with SFT and GRPO teachers. We report success rates on ALFWorld and WebShop and accuracy on Search-based QA. The best results are bolded, and the second-best results are underlined.
Table 3
Figure 2: Training efficiency and evaluation performance of SAPD and Online OPD.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Training pool
Offline validation pool
Benchmark
Trajectories
Prefixes
Trajectories
Prefixes
ALFWorld
2,382
21,300
123
1,132
WebShop
909
3,384
45
166
Search-based QA
8,146
22,826
428
1,198
Appendix
Table 4: Statistics of the offline trajectory pools. Offline validation pools are distinct from the environment episodes and QA evaluation sets used to measure task performance.
Setting
ALFWorld
WebShop
Search-based QA
Student model
Qwen2.5-1.5B-Instruct
Teacher model
Qwen2.5-3B-Instruct
Teacher training
SFT or GRPO
Teacher updates
Frozen throughout distillation
Learning rate
10−6
10−6
10−6
Trajectories per training batch
16
16
128
Appendix
Table 5: Model settings and default training and evaluation configurations.
Figure 3: Evaluation success rates of SAPD and OPD+Rule during training with SFT teachers.
ALFWorld
WebShop
Method
Pick
Look
Clean
Heat
Cool
Pick2
Avg.
Success
ReOPD
92.2
61.1
93.0
88.0
72.9
25.4
75.0
66.7
SAPD
94.2
60.0
85.5
94.0
74.7
20.4
76.6
70.8
Appendix
Table 6: Performance between SAPD and ReOPD. Avg. reports overall success across all evaluation episodes, giving greater weight to categories with more episodes. Best results are bold.
Figure 4: ALFWorld evaluation performance with a 3B student and a GRPO-trained 3B teacher using offline SFT prefixes.
SFT teacher
GRPO teacher
Scoring scope
ALFWorld
WebShop
Search
ALFWorld
WebShop
Search
Action-only
96.6
59.4
38.8
69.3
53.4
41.3
Full-token
95.9
50.0
38.6
60.8
52.3
41.6
Appendix
Table 7: Comparison of action-only and full-token scoring for step advantages. Both variants use offline SFT prefixes and update the complete response. Results are reported in percentages, with the better result in each setting shown in bold.