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

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.

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.

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:

• —currentbestbpb,
• —currentbestrun,
• —old run vs new verified run,
• —choosing the canonical log,
• —one next action instead of many branches.

Structure

Each record contains:

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

Splits

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

Total: 40,000 examples.

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.

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.

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.