simpo-loss
From the author
Implement the SimPO (Simple Preference Optimization) loss function from the NeurIPS 2024 paper. Use this skill whenever implementing SimPO, preference optimization losses, or DPO variants that use length-normalized rewards and reward margins.
Install
npx skills add cxcscmu/SkillLearnBench --skill simpo-loss -a claude-code
Installs to .claude/skills/simpo-loss/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
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Why 83
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Similar skills
Closest to this one by what they do and when they are used.
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- simpo-loss-functioncxcscmu/SkillLearnBenchImplement SimPO loss with length-normalized rewards and target margin.Other★ 91
- evo-simpo-code-reproductionOpenLAIR/OpenSkillImplements the SimPO (Simple Preference Optimization) loss function and handles environment setup, execution, and result saving for code reproduction tasks.Engineering★ 97
- preference-optimizationwshobson/agentsAlign a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.Engineering★ 41.7K
- simpo-trainingKalarisLabs/research-agent-skillsSimple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.Other★ 61
- grpo-rl-trainingKalarisLabs/research-agent-skillsGuides GRPO (Group Relative Policy Optimization) fine-tuning of language models with the TRL library, including GRPOTrainer configuration, composing multiple reward functions (correctness, format, length, style), dataset prep in chat format, Unsloth setup, LoRA merging, and monitoring reward, reward_std and KL. Use when training a model to follow a strict output format such as XML or JSON. Use when teaching verifiable tasks like math or code with objective correctness rewards. Use when improving chain-of-thought reasoning with custom reward functions. Use when debugging flat rewards, mode collapse, or OOM in GRPO runs. Do not use for plain supervised fine-tuning (use SFT) or when high-quality preference pairs exist (use DPO or PPO).Engineering★ 61
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