Building Verifiable Skills for Reinforcement Learning
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Key Takeaways
- SkillForge achieves a 16.0 success rate improvement on ALFWorld over the GRPO baseline.
- The method outperforms SkillRL by 6.3% on average by using continuous verification of agent skills.
- The system utilizes Qwen2.5-7B-Instruct to achieve high scores across ALFWorld, WebShop, and AppWorld benchmarks.
- Skill bank growth and increased token usage for explicit skill calls are primary factors to consider for production deployment.
Summary & Methodology Analysis
SkillForge addresses the limitations of standard reinforcement learning, where agents struggle to retain knowledge across episodes. The architecture builds a central skill bank that is initialized from teacher-distilled trajectories. During interaction, the system retrieves a compact set of relevant skills and injects them into the system prompt. The model explicitly invokes these skills using structured tags, allowing for traceable action sequences during execution. Training involves the joint optimization of both environment actions and skill selection using GRPO (Group Relative Policy Optimization, a reinforcement learning algorithm that improves stability by comparing multiple policy outcomes).
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the main goal of SkillForge?
To enable reinforcement learning agents to evolve and reuse verifiable skills across different tasks.
Q2. Does this approach improve agent success rates?
Yes, it achieved a 16.0 success rate improvement over the GRPO baseline on the ALFWorld benchmark.
Q3. What benchmarks were used to test this system?
The researchers evaluated the system using ALFWorld, WebShop, and AppWorld.
Q4. How does SkillForge perform compared to SkillRL?
Without cold-start initialization, SkillForge outperformed SkillRL by 6.3% on average.
Q5. What are the potential drawbacks regarding inference costs?
The explicit skill calling design introduces additional tokens during interactions, which increases both prompt length and overall inference costs.
Q6. Is the skill bank size static?
No, the skill bank may grow over time as new skills are continuously induced, which can lead to increased retrieval overhead in long training runs.
Q7. What influences the quality of the learned skills?
The quality depends on the capabilities of the external teacher LLM used to synthesize and revise skills from trajectory abstractions.
Q8. What hardware was required for training?
Qwen2.5-7B-Instruct and Qwen3-4B-Instruct were trained on one node with 8 NVIDIA H20 GPUs, while Qwen3-30B-A3B-Instruct required 16 H20 GPUs.
Q9. Does the system maintain skill quality automatically?
Yes, it uses a verification mechanism to track performance, though the paper does not specify the exact internal thresholds used for this tracking.