Curriculum learning for agent training — difficulty-scored stages
Project description
AgentCurriculum
Curriculum learning for coding agents — train through progressive difficulty stages.
Overview
AgentCurriculum ranks agent traces by difficulty and builds a 5-stage training curriculum where the model learns basic tool use first, then progressively harder multi-step reasoning and error recovery.
Stages
| Stage | Name | Difficulty | Tools | Errors | LR | LoRA r |
|---|---|---|---|---|---|---|
| 1 | Basic | 0.0–0.2 | <5 | 0 | 2e-4 | 64 |
| 2 | Intermediate | 0.2–0.4 | <15 | ≤2 | 1e-4 | 64 |
| 3 | Advanced | 0.4–0.6 | <30 | ≤5 | 5e-5 | 32 |
| 4 | Expert | 0.6–0.8 | <60 | ≤10 | 3e-5 | 32 |
| 5 | Master | 0.8–1.0 | <100 | ≤20 | 1e-5 | 16 |
Installation
pip install agent-curriculum
Quick Start
from agent_curriculum import DifficultyScorer, StageBuilder, CurriculumTrainer
# Score traces by difficulty
scorer = DifficultyScorer()
scores = scorer.score_file("traces.jsonl")
# Build curriculum stages
builder = StageBuilder(scorer=scorer)
stages = builder.build_stages("traces.jsonl")
builder.generate_configs("configs/")
# Train through the curriculum
trainer = CurriculumTrainer(base_model="Qwen/Qwen2.5-14B")
results = trainer.train_curriculum("traces.jsonl", start_stage=1, end_stage=5)
License
MIT
Ecosystem
Part of the FableForge ecosystem — 21 open-source projects built from 210K real agent traces:
| Project | Description |
|---|---|
| Anvil | Self-verified coding agent |
| VerifyLoop | Plan→Execute→Verify→Recover framework |
| ErrorRecovery | Self-healing middleware (3,725 error patterns) |
| FableForge-14B | The fine-tuned 14B model (4-stage training) |
| ShellWhisperer | 1.5B edge agent (phone/RPi, 50ms) |
| ReasonCritic | Verification model (130 benchmark tasks) |
| TraceCompiler | Compile traces → LoRA skills |
| AgentRuntime | Persistent agent daemon (systemd for AI) |
| AgentSwarm | Multi-agent from real trace transitions |
| AgentTelemetry | Datadog for agents (token tracking, costs) |
| BenchAgent | HumanEval for tool-use (107 tasks) |
| AgentDev | VSCode extension with verification |
| TraceViz | Trace replay visualizer (Next.js) |
| AgentSkills | npm for agent behaviors |
| AgentCurriculum | 5-stage progressive training |
| AgentFuzzer | Adversarial testing for agents |
| AgentConstitution | Safety guardrails from traces |
| CostOptimizer | Token cost reduction (50-80%) |
| AgentProfiler | Behavioral fingerprinting |
| TrajectoryDistiller | Trace→training data pipeline |
| Fable5-Dataset | HuggingFace dataset release |
Project details
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