TL;DR
Skill1 unifies skill selection, utilization, and distillation for LLM agents through a single policy with shared task-outcome signals. By decomposing outcomes into trends and variations, it achieves 97.5% success on ALFWorld, surpassing RetroAgent by 2.6 points.
Skill1 unifies skill selection, utilization, and distillation for LLM agents through a single policy with shared task-outcome signals. By decomposing outcomes into trends and variations, it achieves 97.5% success on ALFWorld, surpassing RetroAgent by 2.6 points.
47
已证实
0
证据不足
3
无法验证
N/A
可复现性
核心问题
How can skill-augmented LLM agents be trained more effectively when existing methods optimize skill selection, utilization, and distillation separately with inconsistent reward signals?
核心方法
{'approach': 'Skill1 trains a single policy to co-evolve skill selection, utilization, and distillation using a shared task-outcome signal decomposed into low-frequency trends (guiding selection) and high-frequency variations (guiding distillation). The framework uses GRPO optimization with Qwen2.5-7B-Instruct as the initial policy and all-MiniLM-L6-v2 as the frozen encoder.', 'key_components': ['Skill1 trains a single policy to co-evolve all three capabilities toward a shared objective.', 'The method consists of three components: workflow description, reward derivation, and joint optimization formulation.', 'All learning signals are derived from the task outcome r(τ).'], 'section_ids': ['sec_2']}
论点验证
已证实 (85%)
We present Skill1, a framework that achieves unified evolution of skill-augmented agents by training a single policy to co-evolve skill selection, utilization, and distillation.
The paper provides a complete methodological specification in Section 3, including the workflow (§3.1), learning signal derivations (§3.2), and joint optimization objective (§3.3). Experimental validation in Tables 1-2 and Figures 4-6 demonstrates th
The paper provides a complete methodological specification in Section 3, including the workflow (§3.1), learning signal derivations (§3.2), and joint optimization objective (§3.3). Experimental validation in Tables 1-2 and Figures 4-6 demonstrates th
已证实 (85%)
We achieve co-evolution of all three capabilities through credit assignment on a single task-outcome signal r(τ).
The paper explicitly derives learning signals for all three stages from the single task-outcome r(τ): utilization reward (Eq. 8), selection reward via trend (Eq. 5-7), and distillation reward via variation (Eq. 9). The ablation study (Table 2, Figure
The paper explicitly derives learning signals for all three stages from the single task-outcome r(τ): utilization reward (Eq. 8), selection reward via trend (Eq. 5-7), and distillation reward via variation (Eq. 9). The ablation study (Table 2, Figure
已证实 (90%)
To credit selection and distillation, we decompose this signal into its low-frequency trend and high-frequency variation.
The decomposition is mathematically specified: Eq. 5 defines the low-frequency trend as exponential moving average, and Eq. 9 defines the high-frequency variation as deviation from the trend. The paper provides clear formal definitions.
The decomposition is mathematically specified: Eq. 5 defines the low-frequency trend as exponential moving average, and Eq. 9 defines the high-frequency variation as deviation from the trend. The paper provides clear formal definitions.
已证实 (90%)
The low-frequency trend is defined as the moving average of outcomes associated with each skill. This term reflects skill utility and guides the policy toward consistently effective skills.
Equation 5 formally defines the trend as exponential moving average: U(s) ← α·r(τ) + (1-α)·U(s). The paper explains this reflects skill utility and guides selection toward effective skills.
Equation 5 formally defines the trend as exponential moving average: U(s) ← α·r(τ) + (1-α)·U(s). The paper explains this reflects skill utility and guides selection toward effective skills.
已证实 (90%)
The high-frequency variation is approximated with the deviation of the current outcome from the trend. This term captures whether a newly distilled skill improves upon the library's current boundary, and rewards the policy for producing useful skills.
Equation 9 formally defines the variation as R_distill = r(τ) - Û, where Û is the best available utility. The paper explains this captures whether new skills improve upon the library's boundary.
Equation 9 formally defines the variation as R_distill = r(τ) - Û, where Û is the best available utility. The paper explains this captures whether new skills improve upon the library's boundary.
已证实 (90%)
Given a new task, the policy first generates a natural-language query to retrieve candidate skills from the library, and then re-ranks the retrieved candidates to select the best match.
The workflow is fully specified in Section 3.1: query generation (paragraph 14), retrieval via frozen encoder, and re-ranking via permutation generation (paragraph 15).
The workflow is fully specified in Section 3.1: query generation (paragraph 14), retrieval via frozen encoder, and re-ranking via permutation generation (paragraph 15).
已证实 (90%)
A new skill is admitted to B only when r(τ) = 1. When the library reaches its capacity |B| = Nmax, the skill with the lowest retirement score U(s) • log n(s) is removed, where n(s) is the number of times s has been selected.
Paragraph 17 explicitly specifies: skills admitted only when r(τ)=1, and retirement score U(s)·log n(s) used for eviction when capacity reached.
Paragraph 17 explicitly specifies: skills admitted only when r(τ)=1, and retirement score U(s)·log n(s) used for eviction when capacity reached.
已证实 (90%)
We maintain the trend of each skill as a per-skill utility score, updated after each rollout via exponential moving average.
Equation 5 in paragraph 21 provides the exact exponential moving average update formula for per-skill utility scores.
Equation 5 in paragraph 21 provides the exact exponential moving average update formula for per-skill utility scores.
已证实 (90%)
We use normalized discounted cumulative gain (NDCG) as the rubric for re-ranking supervision.
Equation 7 in paragraph 23 defines NDCG as the re-ranking supervision rubric.
Equation 7 in paragraph 23 defines NDCG as the re-ranking supervision rubric.
已证实 (90%)
We approximate distillation reward with the variation of the current outcome relative to the library's trend: R_distill = r(τ) - Û, where Û is the highest trend among the retrieved candidates.
Equation 9 in paragraph 24 formally defines R_distill = r(τ) - Û, where Û is the highest trend among retrieved candidates.
Equation 9 in paragraph 24 formally defines R_distill = r(τ) - Û, where Û is the highest trend among retrieved candidates.
已证实 (85%)
Skill1 achieves 97.5% success rate on ALFWorld, surpassing all other baseline skill-augmented agents.
Table 1 shows Skill1 achieves 97.5% on ALFWorld, the highest among all methods including skill-augmented agents (RetroAgent 94.9%, SkillRL 89.9%). Results averaged over 3 runs with statistical analysis in Appendix D.
Table 1 shows Skill1 achieves 97.5% on ALFWorld, the highest among all methods including skill-augmented agents (RetroAgent 94.9%, SkillRL 89.9%). Results averaged over 3 runs with statistical analysis in Appendix D.
已证实 (85%)
On ALFWorld, Skill1 reaches 97.5% average success rate, surpassing the previous best RetroAgent by 2.6 points and ranking first on five out of six task types.
Table 1 confirms 97.5% vs RetroAgent 94.9% (2.6 point gap). Per-task breakdown shows Skill1 ranks first on 5/6 types (Pick, Pick2, Look, Heat, Cool), with Clean being the exception.
Table 1 confirms 97.5% vs RetroAgent 94.9% (2.6 point gap). Per-task breakdown shows Skill1 ranks first on 5/6 types (Pick, Pick2, Look, Heat, Cool), with Clean being the exception.
已证实 (80%)
On WebShop, Skill1 also demonstrates the best performance across all methods.
Table 1 shows Skill1 achieves 45.6% on WebShop, which is the highest among all compared methods (next best is GiGPO at 42.3%).
Table 1 shows Skill1 achieves 45.6% on WebShop, which is the highest among all compared methods (next best is GiGPO at 42.3%).
已证实 (85%)
Skill1 surpasses GiGPO by 6.7 points, with the largest gains on Look and Pick2 where composing multiple sub-procedures benefits most from reusable skills.
Table 1 shows Skill1 97.5% vs GiGPO 90.8% = 6.7 points. Per-task analysis confirms largest gains on Look (97.5 vs 88.3 = 9.2 pts) and Pick2 (96.7 vs 87.5 = 9.2 pts).
Table 1 shows Skill1 97.5% vs GiGPO 90.8% = 6.7 points. Per-task analysis confirms largest gains on Look (97.5 vs 88.3 = 9.2 pts) and Pick2 (96.7 vs 87.5 = 9.2 pts).
已证实 (90%)
Removing the library entirely causes the largest drop, from 97.5% to 80.9%, with Heat and Pick2 losing over 28 points each.
Table 2 shows w/o Library drops to 80.9% from 97.5% (16.6 point drop). Heat and Pick2 both drop from 96.7 to 68.3 (28.4 points each), confirming the claim.
Table 2 shows w/o Library drops to 80.9% from 97.5% (16.6 point drop). Heat and Pick2 both drop from 96.7 to 68.3 (28.4 points each), confirming the claim.
已证实 (90%)
Removing distillation while keeping the library still reduces performance by 5.1 points.
Table 2 shows w/o Distillation at 92.4% vs full 97.5% = 5.1 point reduction, exactly as claimed.
Table 2 shows w/o Distillation at 92.4% vs full 97.5% = 5.1 point reduction, exactly as claimed.
已证实 (90%)
Without selection the average drops by 5.7 points, concentrated on Heat and Pick2 where routing to the correct multi-step skill matters most.
Table 2 shows w/o Selection at 91.8% vs full 97.5% = 5.7 point drop. Heat and Pick2 show largest drops (6.7 points each), consistent with the claim.
Table 2 shows w/o Selection at 91.8% vs full 97.5% = 5.7 point drop. Heat and Pick2 show largest drops (6.7 points each), consistent with the claim.
已证实 (90%)
Setting λ1=0 or λ2=0 individually reduces performance by 3.5 and 2.6 points respectively. Removing both yields a sharper decline to 90.2%.
Table 2 confirms: λ1=0 yields 94.0% (3.5 pt drop), λ2=0 yields 94.9% (2.6 pt drop), both=0 yields 90.2%.
Table 2 confirms: λ1=0 yields 94.0% (3.5 pt drop), λ2=0 yields 94.9% (2.6 pt drop), both=0 yields 90.2%.
已证实 (80%)
Selection precision converges first, reaching 0.95 by step 20. Both utilization and distillation reaching 0.8 by step 60.
Figure 4 shows training dynamics. Paragraph 47 reports selection precision reaches 0.95 by step 20, and utilization/distillation reach 0.8 by step 60. These are specific quantitative measurements from the figure.
Figure 4 shows training dynamics. Paragraph 47 reports selection precision reaches 0.95 by step 20, and utilization/distillation reach 0.8 by step 60. These are specific quantitative measurements from the figure.
已证实 (80%)
Full Skill1 reaches Û of 0.91 by step 85 while both ablations lag by approximately 0.10.
Figure 5 tracks Û over training. Paragraph 53 reports full Skill1 reaches 0.91 by step 85 while ablations lag by ~0.10.
Figure 5 tracks Û over training. Paragraph 53 reports full Skill1 reaches 0.91 by step 85 while ablations lag by ~0.10.
... 共 50 个论点
可复现性评估
较低可复现性 (0%)
缺失的复现细节
- Code repository not available - no implementation code provided
- Data repository not available - training/evaluation data not accessible
- Appendix C hyperparameters referenced but not provided in available content
- Random seeds not specified for reproducibility
- Hardware specifications missing (GPU type, number of GPUs, memory requirements)
- Training duration/epochs/steps not specified
- Batch size not mentioned (only group size G=16 for GRPO)
- Number of training samples and exact data splits not detailed
- GRPO implementation details not fully specified
- Skill library initialization and management mechanisms not fully described
局限性（作者自述）
- Our evaluation is limited to two representative text-based agent environments. Whether the co-evolution framework generalizes to more environments (e.g., deep search environments) or those with visual observations remains unexplored.
- The library capacity in this work is capped at 5,000 entries. As the diversity of tasks grows, the fixed-size library may become a bottleneck, and more sophisticated eviction or hierarchical organization strategies may be required.
- A natural next step is to extend this lifecycle to hierarchical or multi-agent settings, where skill sharing and conflict resolution introduce new challenges for unified credit assignment.
本分析由 PDF 阅读助手 自动生成，仅供参考，不构成学术评审意见。验证结论和可复现性评估基于论文文本自动分析，可能存在偏差。原始论文请参阅 arXiv。
分析时间：2026-05-13T07:12:52+00:00 · 数据来源：Paper Collector