8Planetterraforming/Parameter-Golf-V5

Solutions Training V5

Overview

Solutions Training V5 is a large auxiliary dataset built around one core idea:

the model should not waste probability mass on unnecessary operations, blind guessing, or brute-force symbolic expansion.

V5 was designed mainly from user-provided failure examples.
Its central focus is not generic reasoning, but entropy reduction in places where language models often become noisy:

1. trying to answer without enough context,
2. forgetting the latest verified project state,
3. overcomputing or expanding huge symbolic numeric patterns instead of transforming them compactly.

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Core V5 idea

A key motivating example is a very large integer ending with 123.

The important behavior is:

• do not guess missing digits,
• do not mentally expand or brute-force the number,
• do not invent magnitude details,
• instead:
  • detect the pattern,
  • preserve the exact suffix,
  • represent the rest symbolically or by compact magnitude.

This generalizes to many other tasks:

• powers of two,
• repeated ×8 transformations,
• cube-volume rules,
• exact filenames,
• procedural commands,
• state-tracking across many earlier messages.

The V5 objective is therefore:

compress, transform, and preserve structure instead of over-generating.

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Why this matters

Language models often lose quality not because they know nothing, but because they:

• perform too many unnecessary operations,
• answer too early,
• expand details that should remain symbolic,
• drift away from the most recent verified state.

That creates extra entropy and weakens next-token prediction quality.

V5 is designed to train the opposite behavior:

• ask only necessary clarifying questions,
• operate on invariants,
• use one canonical source of truth,
• preserve exact symbolic content,
• keep answers short when short is better.

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V5 task families

1. Compression reasoning

This is the dominant V5 theme.

The model should learn:

• not to brute-force huge symbolic numeric patterns,
• not to expand very large integers unnecessarily,
• to preserve exact suffixes and prefixes,
• to reason with invariants such as:
  • doubling a cube side multiplies volume by 8,
  • powers of two should stay in compact structured form,
  • symbolic compression is better than noisy arithmetic narration.

Representative themes:

• powers of two,
• ×8 geometric progression,
• cube-volume scaling,
• symbolic handling of huge integers ending with 123,
• shortcut arithmetic,
• structure-preserving transformations.

2. Controlled answering

The model should learn:

• not to guess under missing context,
• to ask clarifying questions before recommending,
• to distinguish verified facts from assumptions,
• to keep answers short and useful,
• to avoid unnecessary generative drift.

Representative themes:

• appearance / recommendation questions with missing variables,
• anti-hallucination behavior,
• asking for the most important missing inputs first,
• one-step-at-a-time procedural help.

3. State tracking

The model should learn:

• to preserve the newest verified project state,
• to ignore stale historical context when a newer canonical result exists,
• to answer from one current source of truth,
• to avoid jumping across outdated runs and logs.

Representative themes:

• current_best_bpb,
• current_best_run,
• old run vs new verified run,
• choosing the canonical log,
• one next action instead of many branches.

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Structure

Each record contains:

• task
• subcategory
• input
• target
• source_theme
• difficulty

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Splits

• train: 36,000
• validation: 2,000
• test: 2,000

Total: 40,000 examples.

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Intended usage

Solutions Training V5 is an auxiliary training corpus.

Recommended starting mixture:

• 99% main corpus
• 1% V5 auxiliary

Then, if stable:

• 97% main corpus
• 3% V5 auxiliary

Do not replace the official main corpus with V5.

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Intended effect

V5 is designed to improve:

• symbolic compression behavior,
• calibration under incomplete information,
• canonical project-state handling,
• exact string and pattern preservation,
• resistance to unnecessary generation.

The hoped-for downstream effect is lower entropy on fragile outputs and better BPB-oriented behavior under structured pressure.

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Summary

V5 teaches the model:

• do not brute-force when you can transform
• do not guess when you can clarify
• do not drift when a canonical state exists
• do not expand when symbolic compression is enough

This is the core philosophy of Solutions Training V5.