Agentic Skill Discovery

Version: 1.0
Domain: Autonomous Learning Systems, AI Architecture, Reinforcement Learning

Core Concept

Autonomous skill discovery solves the chicken-egg problem: learning what constitutes success while learning how to achieve it. The system must be both student and teacher, creating circular dependencies that require architectural separation of proposal and validation.

Decision Points

Architecture Selection Decision Tree

If task has ground-truth success criteria (e.g., navigation, manipulation with clear goals) → Use Single-Model Architecture with fast LLM evaluation → Cost: Low | Precision: ~75% | Use case: Predetermined tasks

If task requires open-ended skill discovery (e.g., creative tasks, exploration) → Use Dual-Process Architecture (System 1/System 2) → System 1: Fast LLM evaluation for training loops → System 2: Independent VLM validation for library admission → Cost: High | Precision: ~76% | Use case: Autonomous learning

If complex multi-step behavior is desired → Top-Down Quest Decomposition → Start with failed complex task → decompose into subtasks → learn missing prerequisites → Success rate: 43.75% vs 12.50% for bottom-up chaining

If building skill library from primitives → Bottom-Up Skill Chaining only if primitives are well-matched to end goals → Risk: Combinatorial explosion and missing "middle skills" → Fallback: Switch to top-down when composition fails

If performance plateaus with existing approach → Check for circular dependencies (same model proposing and evaluating) → If detected: Implement architectural separation → If not detected: Add RAG with successful skill patterns for environmental knowledge distillation

Validation Strategy Decision Table

Scenario	Fast Eval (System 1)	Slow Eval (System 2)	Library Admission Rule
Training loops	LLM-generated success functions	None	Not applicable
Skill validation	LLM evaluation	VLM verification	Require both to pass
Library contamination risk	High tolerance	Zero tolerance	System 2 override required
Cost constraints	Optimize for speed	Optimize for accuracy	Asymmetric cost acceptance

Failure Modes

1. Rubber Stamp Evaluation

Symptoms: High reported success rates but poor task completion, library growing rapidly Detection Rule: If same model generates rewards AND evaluates success, you have circular dependency Root Cause: LLM acting as both player and referee creates evaluation bias Fix: Implement architectural separation with independent validator (e.g., VLM for visual tasks)

2. Primitive Explosion Paralysis

Symptoms: Large skill library but inability to solve complex tasks, skills don't compose effectively Detection Rule: If skill count grows but complex task success rate stays flat, you have the "middle skills" problem Root Cause: Bottom-up chaining can't discover skills between primitives and complex goals Fix: Switch to top-down quest decomposition from desired complex behaviors

3. Context-Free RAG Waste

Symptoms: RAG retrieval happening but no performance improvement, treating retrieval as generic examples Detection Rule: If RAG doesn't improve constraint learning (e.g., physics understanding), it's just expensive prompting Root Cause: Missing the environmental knowledge distillation mechanism Fix: Structure skill library as progressive model of environmental affordances, not just example collection

4. Library Contamination Cascade

Symptoms: Performance degrades over time as library grows, false positives compound Detection Rule: If library admission uses same evaluation as training loops, contamination will cascade Root Cause: Bad skills enable bad compositions; library errors compound unlike training noise Fix: Use expensive independent validation for library admission, cheap evaluation for training only

5. Manual Granularity Override

Symptoms: Skills feel disconnected from natural semantic boundaries, forced abstraction levels Detection Rule: If manually specifying skill granularity contradicts LLM proposals, check domain alignment Root Cause: LLMs encode semantic task boundaries from training data Fix: Let system propose skills freely, observe discovered granularity, adjust only for domain-specific needs

Worked Examples

Example 1: Robotic Drawer Opening with Dual-Process Validation

Scenario: Agent must learn to open various drawer types without predefined success criteria.

System 1 (Fast) Process:

1. LLM generates skill proposal: "grasp_drawer_handle"
2. LLM creates success function: drawer_handle_grasped = gripper_distance < 0.02
3. RL training uses this function for 100 episodes
4. LLM evaluates: "Success! Gripper consistently reaches handle"
5. Decision Point: Admit to library? NO - requires System 2

System 2 (Slow) Process:

1. VLM analyzes final video frames from 10 best episodes
2. VLM verdict: "Gripper touches handle but doesn't maintain grasp - fingers don't close"
3. Detection: System 1 false positive caught
4. Action: Reject library admission, refine success function
5. Iteration: Add grasp pressure requirement to success function

Expert vs Novice:

• Novice misses: Trusts LLM self-evaluation, admits poorly learned skill
• Expert catches: Same model can't evaluate what it proposed; needs independent validation

Outcome: Library maintains 76% precision vs 46% without System 2

Example 2: Top-Down Quest Decomposition for Multi-Step Task

Scenario: "Prepare coffee" fails with bottom-up skill chaining despite having primitives like "grasp_cup", "pour_liquid"

Bottom-Up Failure Analysis:

• Available skills: grasp_cup, pour_liquid, move_arm, detect_object
• Composition attempts: All 16 possible 2-skill chains tried
• Missing middle skills: "align_cup_with_spout", "detect_liquid_level", "coordinate_bimanual_pouring"
• Decision Point: Bottom-up can't discover these - they're not primitive compositions

Top-Down Decomposition:

1. Failed task: "prepare_coffee"
2. Decompose into: [heat_water] → [grind_beans] → [align_cup] → [pour_water] → [add_beans]
3. Gap analysis: "align_cup" missing from library
4. On-demand learning: Learn "align_cup" with initial state = end state of "grind_beans"
5. Chaining: Each subtask's success state becomes next task's initial condition

Trade-off Analysis:

• Bottom-up: 12.50% success rate, explores unnecessary skill combinations
• Top-down: 43.75% success rate, learns only needed prerequisites
• Cost: Top-down requires task decomposition capability, bottom-up requires exhaustive exploration

When Top-Down Backfires:

• If task decomposition is wrong, learns skills for incorrect subtasks
• If initial states can't be reliably set, chaining breaks
• Mitigation: Validate decomposition before skill learning, implement state-setting verification

Example 3: RAG Environmental Knowledge Distillation

Scenario: Agent must learn manipulation skills in new environment without explicit physics constraints

Without RAG (25% success rate):

1. LLM generates reward: reward = distance_to_target
2. Agent learns to approach but doesn't account for collision boundaries
3. Failure: No knowledge that gripper must be 5cm from surface before grasping

With RAG (46% success rate):

1. Retrieve previous success: "reach_cube_A" reward function
2. Pattern detected: All successful reaches include gripper_height > 0.05 constraint
3. LLM incorporates: reward = distance_to_target if gripper_height > 0.05 else -10
4. Knowledge distilled: Environmental affordance learned implicitly

Expert vs Novice:

• Novice misses: Treats RAG as example provision, doesn't recognize constraint learning
• Expert catches: RAG enables environmental knowledge distillation through pattern recognition

When RAG Backfires:

• If retrieved patterns are from different environments, wrong constraints learned
• If successful examples are too sparse, patterns not statistically significant
• Mitigation: Filter retrieval by environment similarity, require minimum sample size for pattern extraction

Quality Gates

• Proposal and validation use different model capabilities (e.g., LLM proposes, VLM validates)
• Fast evaluation precision >45% and slow evaluation precision >75% measured on held-out test set
• Library admission requires independent validation, not just training loop success
• Complex task decomposition generates 3-7 meaningful subtasks with clear dependency ordering
• RAG retrieval improves constraint learning by >15% over no-RAG baseline
• Skill granularity analysis shows semantic clustering, not parametric enumeration
• System can detect and recover from circular dependency failure modes
• Library contamination rate <25% verified through independent evaluation
• Top-down decomposition outperforms bottom-up chaining by >2x on complex tasks
• Environmental knowledge distillation demonstrates constraint learning from success patterns

NOT-FOR Boundaries

Do NOT use this skill for:

• Predetermined tasks with known success criteria → Use standard RL with hand-crafted rewards instead
• Single-step manipulation tasks → Use supervised learning with demonstration data instead
• Tasks requiring real-time performance → Dual-process validation too slow; use cached skill library instead
• Domains without visual feedback → VLM validation unavailable; design domain-specific independent validators instead
• Resource-constrained environments → System 2 validation expensive; accept lower precision or use hierarchical validation

Delegate to these skills instead:

• Standard reward engineering → For tasks with clear success metrics
• Imitation learning → For tasks with abundant demonstration data
• Hierarchical RL → For tasks with known skill decomposition
• Meta-learning → For rapid adaptation to new task distributions
• Constitutional AI → For tasks requiring value alignment and safety constraints