# TempSamp-R1 (no-CoT) baseline on ActivityForensics

TempSamp-R1's official code is not released. The paper's algorithm, however,
is a strict subset of what `forensics_grpo` already implements:

| TempSamp-R1 component                         | Where it lives in forensics_grpo                                                |
|-----------------------------------------------|---------------------------------------------------------------------------------|
| Mix-policy sampling (G−1 on-policy + 1 GT)    | `src/open_r1/trainer/grpo_trainer_video_GT_soft.py` lines ~509, 786-787         |
| Non-linear reward shaping (paper Eq. 4)       | `transform_rewards` at `grpo_trainer_video_GT_soft.py:692-704`                  |
| Group-relative advantage (paper Eq. 2)        | `grpo_trainer_video_GT_soft.py` lines 1102-1106 (default branch)                |
| IoU reward                                    | `src/open_r1/reward.py::hungarian_iou_reward` (multi-segment) / `forensics_iou` |

So this baseline = `forensics_grpo` trainer + no-CoT prompt + IoU-only reward
+ all forensics-specific extras disabled. The whole baseline is therefore a
~60-line wrapper plus one launch script — no algorithm is re-implemented.

## What this enforces

`src/tempsamp_main.py` sets `FORENSICS_COT=false` at import time, then
asserts that none of these forensics-specific switches is enabled
(they would diverge from the paper algorithm):

- `FORENSICS_DECOMP_ADV`           – set-level GT-segment advantage decomposition
- `FORENSICS_HUNGARIAN_DECOMP`     – per-PRED-segment token-level advantage
- `FORENSICS_CTA_GRPO`             – counterfactual token-level advantage
- `FORENSICS_FORGERY_AWARE`        – external forgery-head reward shaping
- `FORENSICS_USE_EXTERNAL_VERIFIER`, `FORENSICS_VERIFIER_CONTEXT`

If any of these is true at startup, the script raises.

## On the "use their IoU" requirement

TempSamp-R1's paper IoU is single-span (Charades-style). ActivityForensics is
inherently multi-segment, so this baseline uses the natural multi-segment
extension `hungarian_iou_reward`. The reward FORMULA is the same — set-IoU
via Hungarian matching reduces to plain IoU when |GT|=|pred|=1. Algorithm
(mix-policy + non-linear shaping) is unchanged.

If you want strict single-span IoU instead, switch `--reward_funcs` to `iou`
and add a per-segment splitting step to the dataset (mirroring the
`data_forensics/` adapter in `../time_r1/`).

## Files

- `src/tempsamp_main.py`              entry point (locks no-CoT, asserts no extras)
- `scripts/run_tempsamp_no_cot.sh`    launch script
- this README

## Running

```bash
cd /mnt/local-fast/zhangt/baselines/tempsamp_r1
# edit MODEL_PATH / ANNOT_DIR / VIDEO_ROOT / PREPROC_DIR inside the script first
bash scripts/run_tempsamp_no_cot.sh
```

`FORENSICS_GRPO_ROOT` (default `/mnt/local-fast/zhangt/forensics_grpo`) is on
`sys.path` so the wrapper imports `src.open_r1.grpo_forensics:main` directly.