8Planetterraforming/Parameter-Golf-V5

---
license: mit
task_categories:
- text-generation
- text2text-generation
- question-answering
language:
- en
tags:
- parameter-golf
- calibration
- exactness
- symbolic-compression
- auxiliary-training
- synthetic-data
size_categories:
- 10K<n<100K
pretty_name: "Solutions Training 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.

---

## 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:
- current_best_bpb,
- current_best_run,
- 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.