{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"trl","slug":"trl","name":"TRL","type":"repo","url":"https://github.com/huggingface/trl","page_url":"https://unfragile.ai/trl","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"trl__cap_0","uri":"capability://code.generation.editing.supervised.fine.tuning.sft.with.chat.template.formatting","name":"supervised fine-tuning (sft) with chat template formatting","description":"Trains language models on instruction-response pairs using standard supervised learning with automatic chat template formatting. Extends transformers.Trainer with built-in support for multiple chat formats (ChatML, Alpaca, Llama 2, etc.), handling tokenization, padding, and loss masking for instruction-response boundaries. Supports both single-turn and multi-turn conversations with configurable prompt/response masking to ensure gradients only flow through response tokens.","intents":["Fine-tune a base model on domain-specific instruction-response data","Adapt a pretrained model to follow specific instruction formats","Train models with custom chat templates without manual preprocessing","Scale SFT training across multiple GPUs with gradient accumulation"],"best_for":["Teams building domain-specific instruction-following models","Researchers prototyping alignment baselines before RLHF","Organizations migrating from manual dataset formatting to automated pipelines"],"limitations":["No built-in online learning — requires static dataset loaded before training","Chat template inference requires exact format matching; custom templates need manual registration","Loss masking adds ~5-10% training overhead compared to standard causal LM training","No native support for multi-task learning or curriculum scheduling"],"requires":["Python 3.8+","transformers>=4.34.0","datasets library for data loading","CUDA 11.8+ for GPU training (or CPU fallback)","Model weights in Hugging Face format or local checkpoint"],"input_types":["JSON/JSONL with 'prompt' and 'completion' fields","Hugging Face datasets with 'text' or 'messages' columns","CSV with instruction/response columns"],"output_types":["Fine-tuned model weights (safetensors or PyTorch format)","Training logs with loss curves","Evaluation metrics (perplexity, custom metrics via callbacks)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_1","uri":"capability://code.generation.editing.direct.preference.optimization.dpo.with.reference.model.caching","name":"direct preference optimization (dpo) with reference model caching","description":"Implements DPO training that aligns models to human preferences by directly optimizing the log-likelihood ratio between preferred and dispreferred responses, eliminating the need for a separate reward model. Uses a reference model (frozen copy of the base model) to compute KL divergence penalties, with optional weight sharing to reduce memory overhead. Supports multiple loss variants (standard DPO, IPO, KTO) and automatic reference model synchronization across distributed training.","intents":["Align a model to human preferences without training a separate reward model","Reduce RLHF complexity by replacing PPO with a simpler preference optimization objective","Fine-tune models on preference pairs (chosen/rejected responses) with memory efficiency","Experiment with different DPO loss variants (IPO, KTO, ORPO) on the same dataset"],"best_for":["Teams wanting RLHF-quality alignment without PPO complexity","Researchers comparing preference optimization methods","Organizations with limited compute wanting to avoid dual-model inference"],"limitations":["Requires preference pairs (chosen/rejected) — incompatible with single-response datasets","Reference model must fit in memory alongside training model; weight sharing reduces memory by ~40% but adds synchronization overhead","Assumes preference labels are binary and non-contradictory; no built-in handling for ambiguous preferences","Loss variants (IPO, KTO) have different hyperparameter sensitivity; no automated tuning"],"requires":["Python 3.8+","transformers>=4.34.0","PEFT library for optional LoRA on reference model","GPU with 24GB+ VRAM for 7B models (or use quantization)","Dataset with 'prompt', 'chosen', 'rejected' columns"],"input_types":["JSON/JSONL with prompt, chosen response, rejected response","Hugging Face datasets with preference pair structure","Anthropic HH-RLHF format or similar preference datasets"],"output_types":["Aligned model weights","Training curves showing preference accuracy and KL divergence","Evaluation metrics (win rate vs baseline, response diversity)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_10","uri":"capability://code.generation.editing.process.reward.modeling.prm.for.step.level.feedback","name":"process reward modeling (prm) for step-level feedback","description":"Trains reward models that score intermediate steps in a reasoning process (e.g., math problem-solving steps) rather than final outputs. Supports step-level annotations with automatic aggregation to trajectory-level rewards, and includes utilities for parsing structured reasoning formats (e.g., step-by-step math solutions). Integrates with standard TRL trainers for seamless PRM-based training.","intents":["Train reward models that provide feedback on intermediate reasoning steps","Optimize models for step-level correctness in reasoning tasks","Use PRM scores to guide generation or training of reasoning models","Analyze which steps are most critical for final correctness"],"best_for":["Teams building reasoning-focused models (math, code, planning)","Researchers studying step-level feedback and curriculum learning","Organizations with structured reasoning datasets"],"limitations":["Requires step-level annotations; incompatible with outcome-only feedback","Step parsing is task-specific; no automatic step detection","Aggregation from step to trajectory rewards can be unstable with sparse feedback","No built-in support for partial credit or soft step labels"],"requires":["Python 3.8+","transformers>=4.34.0","Dataset with step-level annotations (prompt, steps, step_labels)","GPU with 12GB+ VRAM for 7B models"],"input_types":["JSON/JSONL with prompt, steps (list), step_labels (list of scores)","Structured reasoning format (e.g., LaTeX math with step markers)","Step parser function: callable(text) -> list[steps]"],"output_types":["Step-level reward model weights","Step-level reward scores for analysis","Trajectory-level aggregated rewards"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_11","uri":"capability://image.visual.vision.language.model.vlm.training.with.image.text.alignment","name":"vision-language model (vlm) training with image-text alignment","description":"Extends TRL trainers to support vision-language models by handling image inputs alongside text, with automatic image tokenization and alignment with text tokens. Supports multiple vision encoders (CLIP, DINOv2, etc.) and integrates with chat templates for multi-modal conversations. Includes utilities for image dataset loading, augmentation, and format conversion.","intents":["Fine-tune vision-language models on image-text instruction pairs","Align VLMs to human preferences using DPO or GRPO with image inputs","Train reward models that score image-text responses","Build multi-modal instruction-following models"],"best_for":["Teams building vision-language models (e.g., visual question answering, image captioning)","Researchers studying multi-modal alignment","Organizations with image-text datasets"],"limitations":["Image tokenization adds ~10-20% training overhead compared to text-only","Vision encoders are typically frozen; no end-to-end vision-language training","Image augmentation is limited to standard transforms; no learned augmentation","Requires careful batch composition to handle variable image sizes"],"requires":["Python 3.8+","transformers>=4.34.0","torchvision for image processing","GPU with 40GB+ VRAM for 7B VLM models","Dataset with image-text pairs"],"input_types":["JSON/JSONL with image_path, prompt, completion","Image files (PNG, JPEG, WebP)","Vision encoder config (model_id, image_size)"],"output_types":["Fine-tuned VLM weights","Training logs with image-text alignment metrics","Generated image-text responses"],"categories":["image-visual","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_12","uri":"capability://automation.workflow.command.line.interface.cli.for.training.without.code","name":"command-line interface (cli) for training without code","description":"Provides a command-line interface for launching training jobs with YAML configuration files, eliminating the need to write Python training scripts. Supports all TRL trainers (SFT, DPO, GRPO, etc.) with automatic argument parsing and validation. Includes utilities for hyperparameter sweeps, distributed training setup, and job submission to cloud platforms.","intents":["Launch training jobs without writing Python code","Run hyperparameter sweeps across multiple configurations","Submit training jobs to cloud platforms (e.g., Hugging Face Spaces, Lambda Labs)","Reproduce training runs from configuration files"],"best_for":["Non-technical users wanting to fine-tune models","Teams standardizing training configurations across projects","Organizations automating training job submission"],"limitations":["CLI is less flexible than Python API; custom loss functions require code","Configuration validation is basic; some invalid configs only fail at runtime","No built-in support for conditional logic or dynamic configuration","Hyperparameter sweep syntax is limited compared to specialized tools (e.g., Ray Tune)"],"requires":["Python 3.8+","TRL installed with CLI extras","YAML configuration file","Model and dataset accessible locally or via Hugging Face Hub"],"input_types":["YAML configuration file with trainer_type, model_name_or_path, dataset_name, etc.","Command-line arguments (override config values)","Hyperparameter sweep specification (grid or random search)"],"output_types":["Trained model weights","Training logs and checkpoints","Sweep results (best hyperparameters)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_13","uri":"capability://automation.workflow.async.grpo.with.decoupled.generation.and.training","name":"async grpo with decoupled generation and training","description":"Implements asynchronous GRPO where generation and training happen on separate GPU processes, decoupling the generation bottleneck from training. Uses a queue-based architecture to pipeline generation and training steps, with automatic load balancing and memory management. Supports both local multi-GPU setups and distributed training across multiple machines.","intents":["Maximize GPU utilization by parallelizing generation and training","Scale GRPO training to larger batch sizes without memory overflow","Reduce training time by overlapping generation and gradient updates","Train with different generation and training batch sizes"],"best_for":["Teams with multi-GPU clusters (4+ GPUs) wanting to maximize throughput","Organizations training large models (13B+) with GRPO","Researchers studying asynchronous RL training"],"limitations":["Async training introduces staleness; policy used for generation lags behind training","Queue management adds complexity; debugging is harder than synchronous training","Memory overhead from maintaining separate generation and training processes","Requires careful tuning of queue sizes and batch ratios for stability"],"requires":["Python 3.8+","transformers>=4.34.0","vLLM>=0.4.0","Multi-GPU setup (4+ GPUs recommended)","Distributed training infrastructure (Ray, Slurm, or similar)"],"input_types":["Prompts for generation","Reward function","Async config: num_generation_processes, queue_size, generation_batch_size"],"output_types":["Trained policy model weights","Training logs with throughput metrics and staleness estimates","Queue statistics for debugging"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_14","uri":"capability://code.generation.editing.reinforce.leave.one.out.rloo.for.policy.gradient.optimization","name":"reinforce leave-one-out (rloo) for policy gradient optimization","description":"TRL implements RLOO, a policy gradient method that generates multiple completions per prompt and uses leave-one-out variance reduction to estimate policy gradients. Reduces variance compared to standard REINFORCE while avoiding the need for a separate value function. Integrates with vLLM for efficient generation and supports custom reward functions.","intents":["train models using policy gradient methods with reduced variance via leave-one-out estimation","optimize for custom reward functions without training a separate value function","compare policy gradient approaches (RLOO vs PPO vs GRPO) on the same task"],"best_for":["teams optimizing policy gradient training with variance reduction","researchers studying leave-one-out variance reduction in RL","organizations training with custom reward functions without value function overhead"],"limitations":["RLOO requires generating multiple completions per prompt (typically 4-8), increasing generation cost","leave-one-out variance reduction is less effective than learned value functions for complex reward landscapes","RLOO convergence is slower than PPO due to higher variance in gradient estimates","no built-in support for importance sampling or other advanced variance reduction techniques"],"requires":["Python 3.9+","transformers>=4.36.0","vLLM>=0.3.0 for generation","GPU with 40GB+ VRAM for 13B models"],"input_types":["prompts","reward function","generation config","RLOO hyperparameters (num_completions, learning_rate)"],"output_types":["trained policy model","training logs with reward curves","policy gradient estimates"],"categories":["code-generation-editing","model-training","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_2","uri":"capability://automation.workflow.group.relative.policy.optimization.grpo.with.vllm.generation.backend","name":"group relative policy optimization (grpo) with vllm generation backend","description":"Implements GRPO, an online RL method that generates multiple responses per prompt, scores them with a reward function, and optimizes the policy using group-relative advantages. Integrates with vLLM for high-throughput batch generation (100+ tokens/sec) and supports both server mode (external vLLM process) and colocate mode (in-process generation with memory management). Handles reward function composition, advantage normalization, and policy gradient updates with optional KL clipping.","intents":["Train models with online RL using custom reward functions (e.g., code execution, math verification)","Scale policy optimization across multiple GPUs with decoupled generation and training","Experiment with different reward signals without retraining a separate reward model","Optimize for task-specific metrics (accuracy, latency, safety) directly during training"],"best_for":["Teams building agents with task-specific reward functions","Researchers optimizing for downstream metrics (code execution, math correctness)","Organizations with multi-GPU clusters wanting to parallelize generation and training"],"limitations":["Requires differentiable or discrete reward function; incompatible with human feedback loops","vLLM server mode adds network latency (~50-100ms per batch); colocate mode requires 2x model memory","Advantage normalization can be unstable with small group sizes (<4 responses per prompt)","No built-in support for off-policy corrections; all training data must be on-policy"],"requires":["Python 3.8+","vLLM>=0.4.0 (for generation backend)","transformers>=4.34.0","GPU with 40GB+ VRAM for 7B models in colocate mode","Custom reward function (callable or registered in TRL)"],"input_types":["Prompts as strings or tokenized tensors","Reward function: callable(responses, **kwargs) -> scores","Configuration: group_size, num_generations, reward_fn"],"output_types":["Trained policy model weights","Training logs with reward statistics, KL divergence, policy loss","Generated responses with scores for analysis"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_3","uri":"capability://code.generation.editing.reward.model.training.with.configurable.loss.functions","name":"reward model training with configurable loss functions","description":"Trains reward models that score responses on a continuous scale, supporting both regression (MSE) and ranking (pairwise margin) objectives. Handles preference pair formatting, automatic reference model loading, and loss variants including Bradley-Terry and Elo-based scoring. Integrates with TRL's data pipeline for automatic chat template formatting and supports both single-model and dual-model architectures.","intents":["Train a reward model to score responses for use in PPO or other RL methods","Evaluate model outputs against human preferences at scale","Build preference-based ranking systems without explicit human annotation","Compare reward model architectures (regression vs ranking) on the same dataset"],"best_for":["Teams building RLHF pipelines that need a separate reward model","Researchers studying preference modeling and ranking","Organizations with large preference datasets wanting to extract signal"],"limitations":["Requires balanced preference pairs; class imbalance (e.g., 90% preferred) degrades performance","Regression-based rewards are unbounded and can produce extreme scores; ranking-based rewards require explicit margin tuning","No built-in calibration; reward scores may not align with true preference probabilities","Reward hacking risk when used in downstream RL — requires careful monitoring"],"requires":["Python 3.8+","transformers>=4.34.0","Dataset with preference pairs (prompt, chosen, rejected)","GPU with 12GB+ VRAM for 7B models","Optional: PEFT for parameter-efficient training"],"input_types":["JSON/JSONL with prompt, chosen, rejected columns","Hugging Face datasets in preference pair format","Anthropic HH-RLHF or similar preference datasets"],"output_types":["Trained reward model weights","Reward scores for evaluation set","Training curves showing preference accuracy and loss"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_4","uri":"capability://code.generation.editing.peft.integration.with.lora.and.quantization.for.memory.efficient.training","name":"peft integration with lora and quantization for memory-efficient training","description":"Integrates Hugging Face PEFT library to enable parameter-efficient fine-tuning using LoRA (Low-Rank Adaptation), QLoRA (quantized LoRA), and other adapters. Automatically handles adapter configuration, merging, and unloading, with seamless integration across all TRL trainers. Supports 4-bit and 8-bit quantization via bitsandbytes, enabling training of 70B+ models on consumer GPUs.","intents":["Fine-tune large models (70B+) on limited VRAM (e.g., 24GB consumer GPUs)","Reduce training memory footprint by 75% compared to full fine-tuning","Train multiple task-specific adapters on the same base model","Merge or unload adapters dynamically during inference"],"best_for":["Individual researchers and small teams with limited GPU budgets","Organizations wanting to train multiple models from a single base checkpoint","Teams building multi-task systems with shared base models"],"limitations":["LoRA rank and alpha hyperparameters require tuning; no automated selection","4-bit quantization adds ~10-15% training time overhead due to dequantization","Adapter merging is not lossless; merged models may have slightly different outputs than adapter-based inference","QLoRA is incompatible with some optimizers (e.g., LAMB); requires SGD or AdamW"],"requires":["Python 3.8+","PEFT>=0.4.0","bitsandbytes>=0.39.0 (for quantization)","transformers>=4.34.0","GPU with 8GB+ VRAM (4-bit) or 16GB+ (8-bit)"],"input_types":["LoRA config: rank, alpha, target_modules, lora_dropout","Quantization config: load_in_4bit, bnb_4bit_compute_dtype","Base model weights in Hugging Face format"],"output_types":["LoRA adapter weights (small, ~50-200MB for 7B models)","Merged model weights (full size)","Adapter configuration for inference"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_5","uri":"capability://automation.workflow.distributed.training.with.accelerate.and.multi.gpu.synchronization","name":"distributed training with accelerate and multi-gpu synchronization","description":"Leverages Hugging Face Accelerate library to abstract away distributed training complexity, supporting data parallelism, distributed data parallelism (DDP), and model parallelism across multiple GPUs/TPUs. Handles gradient accumulation, mixed precision training (fp16/bf16), and automatic loss scaling. All TRL trainers inherit Accelerate integration, enabling single-line scaling from 1 GPU to 8+ GPUs without code changes.","intents":["Scale training from single GPU to multi-GPU setups without rewriting code","Use mixed precision (fp16/bf16) to reduce memory and increase throughput","Accumulate gradients across multiple batches for larger effective batch sizes","Train on heterogeneous hardware (mix of V100, A100, H100) with automatic device mapping"],"best_for":["Teams scaling from prototyping to production training","Organizations with multi-GPU clusters (2-8 GPUs)","Researchers wanting reproducible distributed training without NCCL tuning"],"limitations":["Gradient synchronization adds ~5-10% overhead per training step","Mixed precision training can cause numerical instability with certain loss functions (e.g., very small learning rates)","No built-in fault tolerance; training stops on any GPU failure","Requires careful batch size tuning; effective batch size = per_device_batch_size * num_devices * gradient_accumulation_steps"],"requires":["Python 3.8+","Accelerate>=0.20.0","transformers>=4.34.0","NCCL 2.10+ (for multi-GPU synchronization)","Multiple GPUs with NVLink or high-bandwidth interconnect"],"input_types":["Training config: num_train_epochs, per_device_train_batch_size, gradient_accumulation_steps","Distributed config: mixed_precision (no/fp16/bf16), ddp_find_unused_parameters","Model and dataset (standard PyTorch format)"],"output_types":["Trained model weights (synchronized across all GPUs)","Training logs with per-step loss and throughput","Checkpoints saved to shared storage"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_6","uri":"capability://data.processing.analysis.automated.dataset.formatting.with.chat.templates.and.tokenization","name":"automated dataset formatting with chat templates and tokenization","description":"Provides a unified data pipeline that automatically detects and applies chat templates (ChatML, Alpaca, Llama 2, Mistral, etc.) to raw instruction-response data, handling tokenization, padding, and attention mask generation. Supports multiple input formats (JSON, CSV, Hugging Face datasets) and automatically infers schema from data. Includes utilities for dataset validation, train/test splitting, and format conversion.","intents":["Convert raw instruction-response data to properly formatted training data without manual preprocessing","Switch between different chat templates without reprocessing the dataset","Validate dataset quality and detect formatting issues before training","Combine multiple datasets with different formats into a unified training set"],"best_for":["Teams building instruction-tuned models without data engineering expertise","Researchers experimenting with different chat formats","Organizations with heterogeneous data sources needing unified preprocessing"],"limitations":["Chat template inference requires exact format matching; ambiguous formats may be misdetected","Custom chat templates require manual registration; no automatic template learning","Tokenization is model-specific; switching models may require reprocessing","No built-in handling for multi-modal data (images, audio); text-only"],"requires":["Python 3.8+","transformers>=4.34.0","datasets library for data loading","Model tokenizer (from Hugging Face or local)"],"input_types":["JSON/JSONL with 'prompt' and 'completion' fields","CSV with instruction/response columns","Hugging Face datasets with 'text' or 'messages' columns","Raw text files with custom delimiters"],"output_types":["Tokenized dataset with input_ids, attention_mask, labels","Dataset statistics (token counts, sequence lengths)","Validation report (format issues, outliers)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_7","uri":"capability://automation.workflow.training.callbacks.and.custom.metrics.with.hugging.face.integration","name":"training callbacks and custom metrics with hugging face integration","description":"Provides extensible callback system for monitoring training progress, computing custom metrics, and triggering actions at key points (epoch end, step end, evaluation). Integrates with Hugging Face Hub for automatic model uploading, Weights & Biases for experiment tracking, and TensorBoard for visualization. Callbacks have access to trainer state, model, and optimizer for advanced monitoring.","intents":["Monitor training progress with custom metrics (e.g., preference accuracy, reward statistics)","Automatically upload checkpoints to Hugging Face Hub during training","Track experiments with Weights & Biases or TensorBoard","Implement early stopping or learning rate scheduling based on custom metrics"],"best_for":["Teams wanting detailed training observability without custom logging code","Researchers comparing multiple training runs with experiment tracking","Organizations uploading models to Hugging Face Hub automatically"],"limitations":["Callback execution adds ~1-5% training overhead depending on metric complexity","Custom metrics require manual implementation; no automatic metric discovery","Hub integration requires authentication token; no built-in credential management","Callback ordering is fixed; no support for conditional callback execution"],"requires":["Python 3.8+","transformers>=4.34.0","Optional: huggingface_hub for Hub integration","Optional: wandb for Weights & Biases tracking","Optional: tensorboard for TensorBoard visualization"],"input_types":["Custom callback class extending TrainerCallback","Metric function: callable(predictions, labels) -> dict","Hub config: hub_model_id, hub_strategy (push_best, push_every_save)"],"output_types":["Training logs with custom metrics","Model checkpoints uploaded to Hub","Experiment tracking data in W&B or TensorBoard"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_8","uri":"capability://code.generation.editing.kto.and.orpo.preference.optimization.variants","name":"kto and orpo preference optimization variants","description":"Implements Kahneman-Tversky Optimization (KTO) and Odds Ratio Preference Optimization (ORPO) as alternatives to DPO, using different loss formulations for preference learning. KTO uses a reference model and asymmetric loss weighting to handle imbalanced preferences, while ORPO combines preference optimization with language modeling loss to prevent reward hacking. Both methods support the same preference pair format as DPO but with different hyperparameter sensitivity.","intents":["Align models using KTO when preference data is imbalanced (e.g., 80% preferred)","Use ORPO to prevent reward hacking by combining preference and language modeling objectives","Compare different preference optimization methods on the same dataset","Fine-tune models with asymmetric preference weighting"],"best_for":["Teams with imbalanced preference data (KTO)","Researchers studying reward hacking and alignment robustness (ORPO)","Organizations comparing preference optimization methods"],"limitations":["KTO requires careful tuning of loss weights for imbalanced data; no automated selection","ORPO adds language modeling loss, increasing training time by ~20%","Both methods are newer than DPO; less community experience and fewer hyperparameter guidelines","No built-in support for multi-task preference optimization"],"requires":["Python 3.8+","transformers>=4.34.0","Dataset with preference pairs (prompt, chosen, rejected)","GPU with 24GB+ VRAM for 7B models"],"input_types":["JSON/JSONL with prompt, chosen, rejected columns","Hugging Face datasets in preference pair format","KTO-specific: imbalance ratio (proportion of preferred responses)"],"output_types":["Aligned model weights","Training curves showing preference accuracy and loss","Evaluation metrics (win rate, response diversity)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__cap_9","uri":"capability://automation.workflow.reinforce.leave.one.out.rloo.policy.gradient.training","name":"reinforce leave-one-out (rloo) policy gradient training","description":"Implements RLOO, a variance-reduced policy gradient method that trains models by comparing each response against a baseline computed from other responses in the same batch. Reduces variance compared to standard REINFORCE while avoiding the computational overhead of value function training. Supports both on-policy and off-policy variants with optional importance weighting.","intents":["Train models with policy gradients using variance reduction without a value head","Optimize for task-specific rewards (code execution, math verification) with lower variance","Scale policy gradient training across multiple GPUs with efficient batch utilization","Experiment with leave-one-out baseline estimation"],"best_for":["Teams wanting policy gradient training without value head complexity","Researchers studying variance reduction in RL","Organizations optimizing for task-specific metrics with limited compute"],"limitations":["Requires multiple responses per prompt (typically 4-8); increases generation cost","Baseline estimation is unstable with small batch sizes (<32 responses)","No built-in support for off-policy corrections; all training data must be on-policy","Hyperparameter sensitivity: learning rate and reward scaling require careful tuning"],"requires":["Python 3.8+","transformers>=4.34.0","vLLM>=0.4.0 (for efficient generation)","GPU with 24GB+ VRAM for 7B models","Custom reward function"],"input_types":["Prompts as strings or tokenized tensors","Reward function: callable(responses, **kwargs) -> scores","Configuration: num_generations, learning_rate, reward_scaling"],"output_types":["Trained policy model weights","Training logs with policy loss, reward statistics, variance metrics","Generated responses with scores"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"trl__headline","uri":"capability://model.training.transformer.reinforcement.learning.library","name":"transformer reinforcement learning library","description":"TRL is a comprehensive library for post-training foundation models, utilizing methods like Supervised Fine-Tuning and Direct Preference Optimization, making it a go-to choice for reinforcement learning in natural language processing.","intents":["best reinforcement learning library","Transformer library for model training","best library for RLHF","post-training optimization tools for transformers","RL training framework for NLP models"],"best_for":["developers working on NLP models","researchers in AI alignment"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","transformers>=4.34.0","datasets library for data loading","CUDA 11.8+ for GPU training (or CPU fallback)","Model weights in Hugging Face format or local checkpoint","PEFT library for optional LoRA on reference model","GPU with 24GB+ VRAM for 7B models (or use quantization)","Dataset with 'prompt', 'chosen', 'rejected' columns","Dataset with step-level annotations (prompt, steps, step_labels)","GPU with 12GB+ VRAM for 7B models"],"failure_modes":["No built-in online learning — requires static dataset loaded before training","Chat template inference requires exact format matching; custom templates need manual registration","Loss masking adds ~5-10% training overhead compared to standard causal LM training","No native support for multi-task learning or curriculum scheduling","Requires preference pairs (chosen/rejected) — incompatible with single-response datasets","Reference model must fit in memory alongside training model; weight sharing reduces memory by ~40% but adds synchronization overhead","Assumes preference labels are binary and non-contradictory; no built-in handling for ambiguous preferences","Loss variants (IPO, KTO) have different hyperparameter sensitivity; no automated tuning","Requires step-level annotations; incompatible with outcome-only feedback","Step parsing is task-specific; no automatic step detection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.297Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=trl","compare_url":"https://unfragile.ai/compare?artifact=trl"}},"signature":"aDJw01jcQzpUnMM8L8GO1igg+CbDYbGcE23/JB1Q2smt6qlVTyotLnLOC1n1B3xpQu43cyhTaMBD86vfz2n3CA==","signedAt":"2026-06-21T16:56:58.391Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/trl","artifact":"https://unfragile.ai/trl","verify":"https://unfragile.ai/api/v1/verify?slug=trl","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}