Yifan Yang
77ac33e8bf
The "脑补推演" core the user described — re-run the same task many times and
learn from the contrast between good and bad rollouts:
- rollout.py: multi_rollout(task, k) runs K scored attempts; RolloutSet exposes
best/worst/spread/pass_rate. contrastive_reflect picks the highest-spread
tasks (some attempts passed, some failed — most informative) and asks the
optimizer what the GOOD attempts did that the BAD ones didn't, distilling a
general rule. Far stronger signal than a single failure.
- consolidate(rollouts_k>1) uses contrastive reflection (falls back to
single-shot reflect if it yields nothing).
- budget.py: Budget(max_tokens|max_minutes) tracks spend; plan_depth() derives
(nights, rollouts_k) from a token budget. run_gbrain gains --rollouts-k,
--budget-tokens, --budget-minutes (auto-plans depth).
3 new tests (rollout stats, budget+plan, contrastive stub). 26 tests pass.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
"""SkillOpt-Sleep — budget controller.
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Lets the user say how much they're willing to spend on a night's "dreaming",
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in tokens or wall-clock minutes, and the engine schedules depth (how many
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rollouts × how many nights) within that budget. Stops cleanly when exhausted
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and reports what it skipped (no silent truncation).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Optional
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@dataclass
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class Budget:
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max_tokens: Optional[int] = None # None = unlimited
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max_minutes: Optional[float] = None # None = unlimited
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_start_time: Optional[float] = None
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_tokens_at_start: int = 0
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def start(self, clock_fn, tokens_now: int) -> None:
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self._start_time = clock_fn()
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self._tokens_at_start = tokens_now
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def tokens_spent(self, tokens_now: int) -> int:
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return max(0, tokens_now - self._tokens_at_start)
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def minutes_elapsed(self, clock_fn) -> float:
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if self._start_time is None:
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return 0.0
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return (clock_fn() - self._start_time) / 60.0
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def remaining_fraction(self, *, tokens_now: int, clock_fn) -> float:
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"""Smallest remaining fraction across all active limits (1.0 = fresh)."""
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fracs = [1.0]
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if self.max_tokens:
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fracs.append(max(0.0, 1.0 - self.tokens_spent(tokens_now) / self.max_tokens))
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if self.max_minutes:
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fracs.append(max(0.0, 1.0 - self.minutes_elapsed(clock_fn) / self.max_minutes))
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return min(fracs)
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def exhausted(self, *, tokens_now: int, clock_fn) -> bool:
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if self.max_tokens and self.tokens_spent(tokens_now) >= self.max_tokens:
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return True
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if self.max_minutes and self.minutes_elapsed(clock_fn) >= self.max_minutes:
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return True
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return False
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def status(self, *, tokens_now: int, clock_fn) -> str:
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parts = []
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if self.max_tokens:
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parts.append(f"tokens {self.tokens_spent(tokens_now)}/{self.max_tokens}")
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if self.max_minutes:
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parts.append(f"minutes {self.minutes_elapsed(clock_fn):.1f}/{self.max_minutes}")
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return ", ".join(parts) or "unbounded"
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def plan_depth(budget: Budget, *, n_tasks: int,
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default_nights: int = 2, default_k: int = 1) -> tuple:
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"""Heuristically choose (nights, rollouts_per_task) from a token budget.
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Rough cost model: one rollout ≈ 1 unit; a night does ~n_tasks*k rollouts
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plus reflect/gate (~2*n_tasks). We scale k and nights up with more budget.
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Returns (nights, k). With no budget set, returns the defaults.
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"""
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if not budget.max_tokens:
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return default_nights, default_k
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# assume ~1.5k tokens per rollout as a planning constant
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rollouts_affordable = budget.max_tokens / 1500.0
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per_night = max(1, n_tasks) * 3 # rollouts + reflect + gate, k=1
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nights = max(1, min(4, int(rollouts_affordable // per_night)))
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# spend surplus on more rollouts-per-task (contrastive signal)
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surplus = rollouts_affordable - nights * per_night
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k = max(1, min(5, 1 + int(surplus // max(1, n_tasks))))
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return nights, k
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