# Thinking effort

Inkling supports continuous reasoning-effort conditioning. Set effort to a finite floating-point value from `0.0` inclusive up to, but not including, `1.0`. Larger values generally encourage more reasoning, but do not guarantee longer responses or higher accuracy on every sample.

## Choose an effort level

These presets are useful starting points when sweeping reasoning effort:

| Name       | `none` | `minimal` | `low` | `medium` | `high` (default) | `xhigh` |
|------------|--------|-----------|-------|----------|-------------------|---------|
| Effort     | `0.0`  | `0.1`     | `0.2` | `0.7`    | `0.9`             | `0.99`  |

These are the same scalar values the OpenAI-compatible API uses for its named `reasoning_effort` presets. The Cookbook renderer accepts any finite scalar in the range `[0.0, 1.0)`.

Before creating training data, use [`sample_reasoning.py`](https://github.com/thinking-machines-lab/tinker-cookbook/blob/main/tinker_cookbook/scripts/inkling/sample_reasoning.py) to sweep effort values for your task and choose the value that produces the desired behavior.

## Generate at a fixed effort

Pass your chosen `effort` to the Cookbook renderer:

```
from tinker_cookbook.renderers import Message
from tinker_cookbook.renderers.tml_v0 import TmlV0Renderer
from tinker_cookbook.tokenizer_utils import get_tokenizer

renderer = TmlV0Renderer(get_tokenizer("thinkingmachines/Inkling"))
messages = [Message(role="user", content="Solve this problem step by step.")]  
prompt = renderer.build_generation_prompt(messages, effort=0.9)
```

When `effort` is omitted, the renderer uses high effort (`0.9`). The renderer inserts a `Thinking effort level` system message before the first non-system message. Do not add that message manually or include another effort message in the conversation.

## Supervised fine-tuning

Generate rollouts at your chosen fixed effort, then use the same value when rendering them for supervised fine-tuning:

```
model_input, weights = renderer.build_supervised_example(
    messages_with_assistant_response,
    effort=0.9,
)
```

Generation and supervised rendering insert the same conditioning prefix, so training data matches sampling token-for-token. Generic supervised dataset builders currently use the default effort (`0.9`). To render individual conversations at another effort level, call `build_supervised_example(...)` directly.

We plan to cover variable-effort conditioned training in a future Cookbook guide.

## Behavior to keep in mind

- `effort=0.0` conditions the model toward no reasoning; it does not enforce a hard no-reasoning constraint.
- Reasoning effort and `max_tokens` are independent. Higher effort may require a larger generation budget to avoid truncation.
- Temperature still affects generation.
- Compare several samples or aggregate benchmark accuracy rather than drawing conclusions from one response.

## Low-level renderer

The same control is available through the low-level `tml_renderers.v0.Renderer` (see the [`tml-renderers` API reference](https://tinker-docs.thinkingmachines.ai/cookbook/inkling/tml-renderers/#tml_renderers-api-reference)):

```
from tml_renderers import chat, tokenizers, v0

tml_renderer = v0.Renderer(tokenizers.o200k_base_chat())
tml_messages = chat.OpenAIMessage.from_oss_messages(
    [{"role": "user", "content": "Solve this problem step by step."}]
)
spans, parser = tml_renderer.render_for_completion_with_effort(tml_messages, effort=0.9)
```

## Effort scaling

Sweeping Inkling's effort setting traces model quality against mean generated tokens on Terminal-Bench 2.1, Humanity's Last Exam (HLE), and IFBench. Competing models are shown at their default operating points.