learnprompt Skill 14: LLM Textbook + Practice Environments（LLM教科书 + 练习环境）
Source: https://x.com/karpathy/status/1885026028428681698 | https://x.com/karpathy/status/1960803117689397543 "Take LLMs to school" | "Environments for RL" posts
Core Principle
LLMs learn like students. Give them textbooks, worked problems, and practice gyms.
Karpathy's insight: the missing ingredient in LLM training isn't more parameters — it's practice environments where agents can try → fail → see feedback → try again. Like a student who reads theory but also needs problem sets.
Apply this to your agents: build environments where they can practice a skill with automatic scoring, not just generate output into the void.
The Three Training Data Types
Every good learning environment needs all three:
1. EXPOSITION (pretrain equivalent)
→ Background knowledge, concepts, context
→ The "textbook chapter" the agent reads before practicing
2. WORKED EXAMPLES (SFT equivalent)
→ Complete input-output pairs with reasoning shown
→ "Here's a solved problem — learn the pattern"
3. PRACTICE PROBLEMS with feedback (RL equivalent)
→ Problems with verifiable correct answers
→ Automatic scoring so agent knows how it did
→ Enough variety that memorization doesn't work
Building a Practice Gym (General Template)
For any skill you want an agent to get better at:
#!/usr/bin/env python3
"""
Practice Gym for: [SKILL_NAME]
Karpathy-style RL environment for agent skill development
"""
import json
import random
from typing import Callable
class PracticeGym:
"""
A practice environment where an agent can repeatedly attempt
a task and receive automatic feedback.
"""
def __init__(self, task_generator: Callable, scorer: Callable):
self.task_generator = task_generator # generates new practice problems
self.scorer = scorer # returns 0.0-1.0 score for an attempt
self.history = []
def sample_task(self):
"""Generate a new practice problem."""
return self.task_generator()
def evaluate(self, task, attempt):
"""Score an agent's attempt. Returns dict with score + feedback."""
score = self.scorer(task, attempt)
result = {
"task": task,
"attempt": attempt,
"score": score,
"passed": score >= 0.8,
}
self.history.append(result)
return result
def summary(self):
"""Summarize performance across all attempts."""
if not self.history:
return "No attempts yet."
scores = [r["score"] for r in self.history]
return {
"total_attempts": len(scores),
"avg_score": sum(scores) / len(scores),
"pass_rate": sum(1 for s in scores if s >= 0.8) / len(scores),
"recent_trend": "improving" if scores[-1] > scores[0] else "declining"
}
Practice Environment Design Prompt
Build a practice gym for any skill:
Design a practice environment for training an LLM agent to [SKILL].
Skill description: [WHAT THE AGENT SHOULD GET BETTER AT]
Current performance: [HOW WELL IT DOES NOW]
Target performance: [WHAT GOOD LOOKS LIKE]
Design the gym with:
1. TASK GENERATOR
- Input format: [what the task looks like]
- Variation parameters: [what changes between tasks]
- Difficulty levels: [easy / medium / hard criteria]
- Example task: [concrete example]
2. SCORER
- What makes an attempt correct? (exact match / rubric / functional test)
- Score breakdown: [what earns partial credit]
- Automatic vs human evaluation: [which parts can be automated]
- Example: good attempt vs bad attempt with scores
3. CURRICULUM
- Start: [simplest tasks to build confidence]
- Progress: [how to increase difficulty as performance improves]
- Mastery criterion: [when is the skill "learned"?]
4. FEEDBACK FORMAT
What should the agent receive after each attempt?
- Score (0-1)
- Specific error: [what went wrong]
- Hint for next attempt: [one actionable tip]
Quick Eval Loop Prompt
For rapidly testing an agent on a set of practice problems:
Run an evaluation loop on this agent capability.
Agent task: [WHAT THE AGENT IS SUPPOSED TO DO]
Test cases:
1. Input: [test input 1]
Expected output: [expected 1]
2. Input: [test input 2]
Expected output: [expected 2]
3. Input: [test input 3]
Expected output: [expected 3]
For each test case:
1. Attempt the task
2. Compare to expected output
3. Score: PASS / PARTIAL / FAIL
4. Explain the error (if any) in one sentence
Final report:
- Pass rate: N/3
- Most common failure mode: [pattern in errors]
- Suggested improvement: [one specific fix]
The Curriculum Design Template
Build a structured learning progression:
Design a learning curriculum for an agent to master [SKILL].
Like Karpathy's "take LLMs to school" approach — structure it as:
WEEK 1 — Foundations (exposition):
- Core concepts to internalize
- 3-5 worked examples with full reasoning traces
- Quiz: 5 basic problems with answers
WEEK 2 — Pattern Recognition (practice):
- 20 practice problems, graduated difficulty
- Automatic scoring criteria
- Common error analysis
WEEK 3 — Generalization (RL-style):
- Novel problems the agent hasn't seen
- Real-world variants
- Adversarial examples (edge cases designed to fail)
Mastery test: [describe the final eval that confirms the skill is learned]
Scoring Rubrics for Common Skills
Code Generation
Score 1.0: Correct output, handles edge cases, clean style
Score 0.8: Correct output, misses 1 edge case
Score 0.5: Core logic correct, fails on some inputs
Score 0.2: Wrong approach but partially useful
Score 0.0: Doesn't compile or clearly wrong
Reasoning/Analysis
Score 1.0: Correct conclusion + correct reasoning chain + appropriate confidence
Score 0.8: Correct conclusion, minor reasoning gap
Score 0.5: Correct conclusion, wrong reasoning
Score 0.2: Wrong conclusion but shows relevant knowledge
Score 0.0: Wrong conclusion, no relevant reasoning
Following Instructions
Score 1.0: All instructions followed, output matches spec exactly
Score 0.8: Minor deviation from spec, intent preserved
Score 0.5: Core task done, 1-2 instructions ignored
Score 0.2: Significant departure from instructions
Score 0.0: Instructions ignored entirely
Environment-in-a-Gist
Karpathy's pattern: publish the environment spec (not the implementation) so anyone can build their gym:
Write a Gist-style environment spec for [SKILL_GYM].
Format:
# [GYM_NAME] — Practice Environment Spec
## What this trains
## Task format
## Scoring criteria
## Sample task + ideal response
## Curriculum (3 stages)
## How to evaluate mastery
This is a spec, not code. Anyone with an LLM should be able to implement it.
Workflow
属于工作流：月度体检（第3步）
| 位置 | 上游 | 下游 |
|---|---|---|
| 第3步（练习） | karpathy-understanding-first（识别盲区后） | karpathy-education-first（把练习成果教学化） |
完整链路：meta-reflection → understanding-first → practice-environments → education-first
Prompt Contract
Build a practice environment for <SKILL_OR_CAPABILITY>. Include: 1) Exposition — what this skill is and why it matters (3-5 sentences), 2) Worked examples — 2-3 complete input→output demonstrations, 3) Practice tasks — 5 exercises with increasing difficulty, 4) Automatic grading — for each task define pass/fail criteria that can be checked without human judgment, 5) Retry loop — if failed, what feedback to give and how to adjust difficulty.
Verification Checklist
- 练习环境有明确的技能定义（不是模糊的「变强」）
- Worked examples 覆盖了典型和边界情况
- 评分标准不需要人工判断（自动化）
- 失败时有具体反馈（不只是「错了」）
- 难度有递进（不是一步到位）
- Agent 可以自主重试直到通过