best-of-n

name	best-of-n
description	Run an agent N times in parallel against the same prompt, then aggregate via one of 5 modes — critic argmax, USC consistency, confidence-weighted, hybrid, or AggAgent synthesis. Use for high-stakes invocations where you'd rather pay Nx to be sure. Triggers on /best-of-n, "best of n", "run multiple", "give me three options", "synthesize across rollouts".
when_to_use	When the cost of being wrong > Nx the cost of running. Verifier on a critical fix. Security review on auth code. Migration plan for production DB. For deep-research-style tasks where rollouts have complementary insights, prefer --mode synthesize (AggAgent).
allowed-tools	Bash, Read
arguments	["agent","prompt","n","mode"]

/best-of-n — Test-time scaling via parallel agent rollouts

/best-of-n <agent> "<prompt>" [n=3] runs the named agent N times in parallel, scores each rollout via parsed RESULT_<agent>= lines (and critic where applicable), and returns the highest-scoring output.

This is test-time RL: instead of trusting one rollout, sample many and pick by reward. Cost scales linearly with N.

How

python3 ~/.claude/rl/best_of_n.py <agent> "<prompt>" --n <N>

<N> must be in [2, 8]. Default 3.

Aggregation modes

--mode	What it does	Cost	When to use
critic (default)	argmax of critic-derived score	N calls	Routine high-stakes runs
usc	Universal Self-Consistency: pick the rollout most consistent with the others (arXiv 2311.17311)	N+1 calls	Free-form output where critic scores are noisy
confidence	Weight critic_score by self-reported confidence (ReConcile, arXiv 2309.13007, +11.4%)	N calls	Agents that emit CONFIDENCE: 0-1 lines
hybrid	Critic narrows to top-3 PASS, USC tiebreaks	N+1 calls	When critic discriminates well and you want consistency
synthesize	AggAgent: synthesize a NEW answer combining the best of all N rollouts (arXiv 2604.11753, +5-10%)	N+1 calls	Deep research, design docs, multi-step reasoning where rollouts have complementary insights

When to pick synthesize over the others

Use synthesize when the task allows multiple correct approaches that complement each other (e.g., "design a system", "review this PR", "summarize these findings"). It treats N rollouts as parallel drafts and produces ONE merged final answer.

Use critic / usc / confidence / hybrid when there's exactly one right answer and you want to PICK from candidates rather than synthesize across them (e.g., "fix this specific bug", "produce this specific test").

The synthesizer emits a 3-section structured response:

• AGREEMENT: bullets of what most/all candidates agreed on
• DIVERGENCE_DECISIONS: per-divergence resolution with rationale
• SYNTHESIZED_FINAL_ANSWER: the merged output (this is what gets used downstream)

Output lands at <out>/synthesized.txt and <out>/decision.json[synthesis].

The runner:

1. Spawns N parallel claude -p invocations, each prefixed "Use the agent for this. "
2. Each rollout writes its full stream-json transcript to ~/.claude/rl/best-of-n-runs/<ts>/run-<i>.jsonl
3. Parses RESULT_<agent>= lines from each rollout
4. Scores: PASS=+1, CLEAN=+0.7, FAIL=-1, INCONCLUSIVE=0; with critic delta if present
5. Picks the highest-scoring rollout; symlinks winner.jsonl
6. Writes decision.json with all rollout scores
7. Emits a single reward event for the decision

Output

{
  "agent": "verifier",
  "n": 3,
  "rollouts": [
    {"idx": 1, "score": 1.0, "results": {"verifier": "PASS"}, "elapsed_s": 12.4},
    {"idx": 2, "score": 1.0, "results": {"verifier": "PASS"}, "elapsed_s": 14.1},
    {"idx": 0,       
  
   
   
   

If spread is large (>0.5), this means rollouts disagreed — useful diagnostic. Trust the winner less in that case; consider going to N=5.

When NOT to use

• Routine task closure — overkill
• When the agent is deterministic-by-design (linter wrappers, doc generators)
• When budget is tight — N rollouts cost ~N× a single
• When you don't have a clear RESULT_<agent>= last line — scoring degrades to 0

Anti-patterns

• Calling /best-of-n on the wrapping skill (e.g., /best-of-n verify ...) — call on the agent (/best-of-n verifier ...)
• Setting N=8 by default — wastes cost. Default 3 is a strong baseline.
• Trusting the winner without reading its evidence — even the best of 3 can be wrong; this is not a substitute for review.
• Calling on agents that don't emit RESULT_<name>= lines — score is meaningless without that contract.