add-dynamic-filter

# Add Dynamic Filter Add filtering hooks in rollout and buffer stages while preserving sample-group contracts. ## When to Use Use this skill when: - User asks to filter sample groups during dynamic sampling - User asks to customize buffer extraction strategy - User asks to mask/remove some rollout samples before training - User asks to process all generated samples for logging/analysis ## Step-by-Step Guide ### Step 1: Pick the Correct Hook - Dynamic sampling filter: `--dynamic-sampling-filter-path` - Buffer filter: `--buffer-filter-path` - Per-sample rollout filter: `--rollout-sample-filter-path` - All-samples post process: `--rollout-all-samples-process-path` ### Step 2: Implement the Function Signature Dynamic sampling filter (called in `slime/rollout/sglang_rollout.py`): ```python def filter_function(args, samples, **kwargs): # return DynamicFilterOutput or bool ``` Preferred return type: ```python from slime.rollout.filter_hub.base_types import DynamicFilterOutput return DynamicFilterOutput(keep=True, reason=None) ``` Buffer filter (called in `slime/data/data_source.py`): ```python def buffer_filter(args, rollout_id, buffer, num_samples): return selected_groups ``` Rollout sample filter: ```python def rollout_sample_filter(args, samples): # modify sample.remove_sample in-place where needed ``` All-samples process: ```python def process_all_samples(args, all_samples, data_source): ... ``` ### Step 3: Preserve Group Structure - Keep `list[list[Sample]]` structure intact where required. - Do not flatten sample groups unless downstream path expects flattened samples. - For sample removal, prefer `sample.remove_sample=True` over deleting objects. ### Step 4: Wire and Validate Example wiring: ```bash --dynamic-sampling-filter-path slime.rollout.filter_hub.dynamic_sampling_filters.check_reward_nonzero_std --buffer-filter-path <module>.buffer_filter --rollout-sample-filter-path <module>.rollout_sample_filter --rollout-all-samples-process-path <module>.process_all_samples ``` ### Step 5: Measure Side Effects - Check final sample count remains aligned with `rollout_batch_size` expectations. - Verify drop reasons are surfaced in rollout metrics when dynamic filter is used. - Validate training still receives valid loss masks/rewards after filtering. ## Common Mistakes - Returning wrong container type for buffer filter - Dropping samples by deletion instead of mask flagging - Losing sample-group alignment in group-RM setup - Adding expensive logic in hot filtering paths ## Reference Locations - Dynamic filter types: `slime/rollout/filter_hub/base_types.py` - Dynamic filter example: `slime/rollout/filter_hub/dynamic_sampling_filters.py` - Rollout generation hook points: `slime/rollout/sglang_rollout.py` - Buffer filter hook point: `slime/data/data_source.py`