mirror of https://github.com/wassname/moral-maps.git synced 2026-09-26 14:00:29 +08:00

Files

T

wassname 0f8048d5d9 Implement N-token evaluation with guided rollouts

- Refactored evaluation logic in `src/tinymfv/eval.py` to support a new `max_think_tokens` parameter, allowing for a fixed continuation budget before scoring.
- Introduced `guided_rollout` function in `src/tinymfv/guided.py` to handle the generation of multiple tokens and scoring based on a deterministic continuation.
- Updated the CLI in `scripts/03_eval.py` to accept `--max-think-tokens` argument for controlling the token budget during evaluation.
- Created a new specification document `docs/spec/20260501_n_token_eval.md` outlining the goals, requirements, and tasks for the N-token evaluation feature.
- Simplified the record creation in `scripts/02_rewrite.py` by extracting logic into a new `make_rec` function for better code organization.

2026-05-01 21:44:14 +08:00

2.7 KiB

Raw Blame History

N-token Evaluation

Goal

Make eval score after a short deterministic continuation instead of only from the immediate next token, so steering effects that emerge over a few tokens show up in the metric.

Scope

In: core eval scoring, evaluate API, eval CLI, smoke verification. Out: changing vignette data, changing report aggregation, adding stochastic rollouts.

Requirements

R1: Eval must support a fixed continuation budget before scoring true/false. Done means: evaluate(..., think_tokens=N) uses a rollout path and records think_tokens=N. VERIFY: a smoke run with N>0 reports mode=guided_rollout and different headline scores than N=0 on the same subset.
R2: Default behavior must stay available for zero-token scoring. Done means: think_tokens=0 still uses the current teacher-forced path. VERIFY: smoke run reports mode=next_token with think_tokens=0.
R3: CLI must expose the token budget. Done means: scripts/03_eval.py --think-tokens N works and writes the budget into output JSON. VERIFY: output JSON contains think_tokens and eval_mode.

Tasks

[/] T1 (R1,R2,R3): patch core scoring and CLI
- steps: add rollout scorer, thread config through evaluate, add CLI arg and metadata
- verify: run eval twice on a tiny subset, once with --think-tokens 0 and once with --think-tokens 8
- success: metadata differs by mode and at least one headline score differs
- likely_fail: flag is accepted but dead code still uses next-token path, scores identical and metadata unchanged
- sneaky_fail: rollout happens but scoring still reads the old prompt position, metadata changes but scores remain indistinguishable from teacher-forced
- UAT: when I run the two eval commands, I see mode + think_tokens in the output JSON and a score delta
T2: fresh-eyes review
- steps: hand diff and smoke evidence to subagent
- verify: reviewer explicitly checks likely and sneaky failure modes
- success: reviewer says evidence distinguishes rollout path from dead code path

Context

Current eval scores only out.logits[:, -1] from the prompt ending at the JSON prefill.
The gist proposes a deterministic guided rollout: generate N tokens, append a fixed answer prefill, then score at the forced answer position.
For this repo we do not need <think> tags. We only need a short continuation budget before the JSON boolean answer.

Log

Guided rollout is the right abstraction here, but the minimal repo change is simpler than the gist: continue the assistant reply for N tokens, then append the existing boolean prefill and score there.

TODO

Consider logging a sample guided continuation when bool mass collapses, but keep that out of this patch unless needed.

Errors

Task	Error	Resolution

2.7 KiB Raw Blame History