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name: optimization-approaches description: "Choose, compose, tune, and verify OptiLLM inference-time optimization approaches such as MoA, BoN, MCTS, CePO, MARS, Z3, reflection, and reranking methods." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Optimization Approaches

Use this sub-skill when the task is to select, combine, tune, or troubleshoot OptiLLM's core inference-time optimization techniques.

Read first for these tasks

• Choose an approach slug for math, coding, long reasoning, logical reasoning, answer selection, or proof tasks.
• Compose approaches with & pipelines or | parallel alternatives.
• Tune MCTS, BoN, n, RStar, CePO, or MARS parameters.
• Understand cost/latency tradeoffs from multi-call approaches.
• Plan benchmark/evaluation runs safely without launching expensive scripts by default.
• Diagnose approach failures with empty, truncated, or provider-incompatible responses.

Route server startup and OpenAI-compatible request plumbing to ../proxy-server/SKILL.md. Route plugins to ../plugins-and-tools/SKILL.md. Route local-only decoding methods to ../local-inference-decoding/SKILL.md.

Approach selection shortcut

• Fast low-risk reasoning: re2, cot_reflection, leap, or plansearch.
• Candidate generation/selection: bon, moa, self_consistency, pvg.
• Search and solver-style tasks: mcts, rstar, z3.
• Math/code competition style: cepo or mars when latency and token budget allow.
• Direct pass-through: none by itself only.

Use provider-compatible choices. Some endpoints do not support multiple completions, which limits bon/parallel sampling workflows.

Composition patterns

moa-gpt-4o-mini                  # one approach
cot_reflection&moa-gpt-4o-mini   # pipeline, left to right
bon|moa|mcts-gpt-4o-mini         # parallel alternatives, returns multiple responses

Approach selection can also come from request body optillm_approach or prompt tags when the server is in auto mode.

Important references and script

• references/approach-catalog.md catalogs verified slugs, signatures, defaults, and task fit.
• references/cepo-and-mars.md covers the advanced CePO and MARS methods, configuration, and evaluation caveats.
• references/evaluation-and-benchmarks.md explains benchmark scripts as reference-only workflows and how to plan safe runs.
• references/troubleshooting.md covers provider limitations, bad responses, token/cost issues, and dependency failures.
• Run python scripts/approach_matrix.py --help to list approaches or parse a model string without provider calls.

Minimal offline validation

python scripts/approach_matrix.py --parse 'bon|moa|mcts-gpt-4o-mini'

Expected parse shape is operation OR, approaches bon,moa,mcts, model gpt-4o-mini.

Tuning checklist

1. Confirm provider supports the request shape (n, system messages, max token fields, streaming).
2. Start with the cheapest approach that addresses the failure mode.
3. Add multi-call approaches only when accuracy matters more than latency/cost.
4. Bound max_tokens, MCTS simulations/depth, BoN candidates, MARS agents/iterations, and CePO planning counts.
5. For proof tasks, preserve full reasoning visibility and be careful with answer extraction or <think> stripping.
6. For code tasks, verify whether answer extraction should preserve code fences.
7. Run safe mock-client tests before real provider benchmarks when editing the repo.

Native evidence anchors

The source repository validates approach imports, mock-client calls, response guards for bad provider outputs, and request-scoped MCTS parameters in safe tests. Benchmark scripts exist for MATH-500, AIME, Arena Hard, FRAMES, SimpleQA, IMO, and OptiLLMBench, but they require datasets, provider calls, and longer runtimes; treat them as explicit evaluation workflows, not default smoke tests.