"""Modality registry -- each task type maps to a verifier and a rubric hint. This is how Handicate improves across ALL the modalities, not just text: every task is tagged with a type, and the reward routes to that type's objective verifier (where one exists) blended with the judge's rubric score. - python / bash / 3d / svg -> objective execution/parse verifier (strong RL signal) - image_prompt / audio_prompt / video_spec -> judge-only (the model learns to write good generation specs; the actual pixels/audio/video come from the dedicated generators -- ImageForge, Michiro, etc. -- which Handicate orchestrates) """ from core import verifiers VERIFIERS = { "python": verifiers.verify_python, "bash": verifiers.verify_bash, "3d": verifiers.verify_3d, "svg": verifiers.verify_svg, "image_prompt": None, "audio_prompt": None, "video_spec": None, } RUBRIC_HINTS = { "python": "Return runnable Python in a ```python block; handle edge cases.", "bash": "Return a correct POSIX bash command/script in a ```bash block.", "3d": "Return Python using trimesh/numpy that builds a variable `mesh` (a valid trimesh).", "svg": "Return a complete ... with real shapes; must parse.", "image_prompt": "Return a vivid, specific text-to-image prompt (subject, style, lighting, detail).", "audio_prompt": "Return a precise music/audio generation prompt (genre, mood, instruments, bpm).", "video_spec": "Return a concrete shot-by-shot video spec (scenes, motion, duration).", } # blend weight: how much the objective verifier counts vs the judge's rubric score VERIFIER_WEIGHT = 0.6 def verifier_for(task_type): return VERIFIERS.get(task_type) def blended_reward(task_type, judge_score, response_text): """Objective verifier (if any) blended with the LLM judge's rubric score.""" vfn = VERIFIERS.get(task_type) if vfn is None: return judge_score try: v = vfn(response_text) except Exception: v = 0.0 return VERIFIER_WEIGHT * v + (1 - VERIFIER_WEIGHT) * judge_score