flowchart LR
  subgraph Trainer["TRL GRPOTrainer (1.5B + LoRA)"]
    M[Qwen 2.5 Coder 1.5B<br/>4-bit + LoRA r=16]
    G[8 generations / step]
    M --> G
  end
  subgraph Env["FATHOM OpenEnv Server (HF Space)"]
    R["REPL primitive<br/>RestrictedPython + subprocess"]
    L["llm() primitive<br/>recursive sub-call"]
    O[Observation: tool output]
  end
  subgraph Reward["Composable Verifier (4 components)"]
    F[format_gate]
    C[correctness]
    T[token_budget alpha-param]
    E[recursion_efficiency]
    F --> X[compose_reward_fn]
    C --> X
    T --> X
    E --> X
  end
  Trainer -- multi-turn rollout --> Env
  Env -- observation --> Trainer
  Trainer -- completion + metadata --> Reward
  Reward -- scalar reward --> Trainer
  style M fill:#1f77b4,color:#fff
  style X fill:#2ca02c,color:#fff