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Future AGI — LLM Fine-Tuning Guide 2026 (LoRA/DPO/GRPO)

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2026-07-09

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Future AGI — LLM-Judge Bias Mitigation (2026)Neutralize LLM-judge bias with the five named-bias playbook before trusting eval scores

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2026-07-09 / SKILLSFine-tuning decision tree for 2026: QLoRA SFT → DPO → GRPO when your reward is verifiableThe 2026 verdict for reasoning/code fine-tuning: start with QLoRA SFT using unsloth on a single H100, layer DPO (or ORPO/KTO) if you have preference pairs, and switch to GRPO once your reward function is verifiable. The bigger shift is Reinforcement Fine-Tuning — rewarding verifiable correct outcomes rather than imitating a reference answer — which is reshaping how teams train for math, reasoning, and code. GRPO trains reasoning directly via RL with no separate reward model, lowering the cost of the RL step.
2026-06-29 / SKILLSHarden your LLM-as-judge mechanically: shuffle, pin, rotate families, calibrate — and apply IRT to the judgesThe 2026 consensus names five judge biases (position, verbosity, self-preference, format, calibration drift) and the mitigations that actually survive production are mechanical: shuffle order on every pairwise call and only count consistent wins, pin the judge contract/version, rotate judges across model families (especially to defeat self-preference), and calibrate against humans. A newer line applies Item Response Theory to the judges themselves — treating reliability as a property of the measurement instrument and flagging rubric criteria that are too ambiguous or too sensitive. Builders running eval pipelines should bake order-swapping and cross-family rotation into the harness rather than trusting a single judge model's score.
2026-06-11 / SKILLSReplace a single LLM judge with a multi-agent debate panel (CollabEval) to surface real issues

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