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https://github.com/microsoft/SkillOpt.git
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cff7ff6846
- Fix teacher/student in deep_reflect, meta_reflect, sealqa, babyvision, mathverse, mmrb, swebench envs and prompt templates - Remove .gradio/certificate.pem from tracked files - Add .gradio/ to .gitignore Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
78 lines
3.2 KiB
Python
78 lines
3.2 KiB
Python
"""Optimizer-written diagnostic probe generation for deep reflection."""
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from __future__ import annotations
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from skillopt.gradient.reflect import fmt_minibatch_trajectories
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from skillopt.model import chat_optimizer
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from skillopt.optimizer.meta_skill import format_meta_skill_context
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from skillopt.prompts import load_prompt
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from skillopt.utils import extract_json
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def generate_deep_probe_instruction(
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skill_content: str,
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items: list[dict],
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prediction_dir: str,
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*,
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system_prompt: str | None = None,
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step_buffer_context: str = "",
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output_requirements: list[str] | None = None,
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meta_skill_context: str = "",
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) -> dict | None:
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"""Generate one minimally-perturbing diagnostic probe instruction."""
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trajectories_text = fmt_minibatch_trajectories(items, prediction_dir)
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if not trajectories_text.strip():
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return None
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actual_system = system_prompt or load_prompt("deep_probe")
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user = (
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f"## Current Skill\n{skill_content}\n\n"
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"## Probe Design Goal\n"
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"Design one short diagnostic instruction to append to the target prompt.\n"
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"The instruction should expose the target's current intermediate judgment\n"
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"without materially changing the original scaffold.\n\n"
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)
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if step_buffer_context.strip():
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user += f"## Previous Steps in This Epoch\n{step_buffer_context}\n\n"
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optimizer_ctx = format_meta_skill_context(meta_skill_context)
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if optimizer_ctx:
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user += optimizer_ctx + "\n\n"
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requirements = output_requirements or [
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"- Some trajectories may include a hidden Reference block. Use it to identify what intermediate conclusion matters, but do not reveal or paraphrase that reference directly to the target.",
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"- The instruction must explicitly request a short <analysis>...</analysis> block before the final <answer>...</answer>.",
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"- Keep the readout concise and structured.",
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"- Do not ask for exhaustive listing, full derivation, or a new solving protocol.",
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"- The instruction text should be ready to append directly to the target's prompt.",
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]
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user += (
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f"## Representative Trajectories ({len(items)} total)\n{trajectories_text}\n\n"
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"## Output Requirements\n"
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+ "\n".join(requirements)
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+ "\n"
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)
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try:
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response, _ = chat_optimizer(
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system=actual_system,
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user=user,
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max_completion_tokens=1024,
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retries=3,
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stage="deep_probe",
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)
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result = extract_json(response)
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if result and str(result.get("probe_instruction", "")).strip():
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parsed = {
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"reasoning": str(result.get("reasoning", "")).strip(),
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"probe_instruction": str(result.get("probe_instruction", "")).strip(),
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}
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if str(result.get("probe_target_id", "")).strip():
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parsed["probe_target_id"] = str(result.get("probe_target_id", "")).strip()
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try:
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if result.get("probe_after_step") is not None:
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parsed["probe_after_step"] = int(result.get("probe_after_step"))
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except Exception: # noqa: BLE001
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pass
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return parsed
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except Exception: # noqa: BLE001
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return None
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return None
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