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Curriculum learning for agent training — difficulty-scored stages

Project description

AgentCurriculum

License: MIT Python 3.10+ Tests

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

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