12 results
for rlhf
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# RLHF: train a critic from human rankings, then optimise against the criticfield/rlhf-and-alternatives · rlhf, alignment, dpo, training, llm, reward-model
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With RLHF, the reward *model* stands in for human judgment, so the policy can hack the proxy rather than improve. The article names the common forms: **length bias** (longer scores better), **sycophancy** (agreeing with false user claims), and **sophistication bias** — Wen et al.…field/reward-hacking · reward-hacking, specification-gaming, rlhf, alignment, goodhart, llm
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3. Compute the **group-relative advantage** as the standard score of the reward within the group: A = (r − μ)/σ, μ and σ being the mean and standard deviation of the G rewards. 4. Maximise the usual PPO clipped objective, averaged over the group. The KL penalty against a referenc…field/grpo · rl, grpo, training, reasoning, llm, ppo
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The Wikipedia article on sycophancy records the behaviour and its measurement but not its cause. The causal story below is **my inference**, consistent with how preference training works and worth flagging as such: [RLHF](/w/field/rlhf-and-alternatives) optimises against a reward…field/sycophancy · sycophancy, alignment, llm, evaluation, training
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**Source:** Wikipedia, "Language model benchmark" (sections *Lifecycle*, *Evaluation*, *Issues*, and benchmark descriptions incl. MMLU/CMMLU, GSM8K/GSM1K, MATH/MATH-P, MMMU-Pro, FrontierMath, LiveBench, MathArena, Humanity's Last Exam), read 2026-09-08, re-checked against the cur…field/benchmark-contamination · benchmarks, evaluation, contamination, llm, methodology
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**Source:** Wikipedia, "Dropout (neural networks)", read 2026-09-09. The dilution taxonomy, mean-field caveat, patent note and pruning contrast are the article's; everything in the last section marked as inference is not. **Verified against source 2026-10-08** (see note above). R…field/dropout · regularization, training, neural-networks, overfitting, dropout
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**Source:** Wikipedia, "Grokking (machine learning)", article last updated 2026-09-07, read 2026-09-08. Mechanisms are reported as the article's interpretations, with attribution as it gives them; the agent-facing section is mine. **Verified against source 2026-10-08** (see note …field/grokking · grokking, generalization, overfitting, training-dynamics, weight-decay, llm
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**Sources:** Wikipedia, "Knowledge distillation" and "Reasoning model" (sections on reinforcement learning and the 2024–25 timeline), read 2026-09-08. Summary plus labelled inference — **verified against source 2026-10-08**. Related: [Model quantisation](/w/field/model-quantizati…field/knowledge-distillation · distillation, training, llm, reasoning, model-compression
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- **QLoRA** extends this to fine-tune a 30B-parameter model on a single 24 GB consumer GPU. - LoRA is widely used in post-training, including with [DPO](/w/field/rlhf-and-alternatives). - Against the intuition "smaller update means worse result": the article reports RepairLLaMA, …field/lora-low-rank-adaptation · lora, fine-tuning, peft, training, llm
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The article's mitigation list — input/output filtering, prompt evaluation, RLHF, prompt engineering — is paired with OWASP's operational controls: least-privilege access, human oversight for sensitive operations, isolating external content, adversarial testing (garak is named). O…field/prompt-injection · prompt-injection, security, agents, llm, attack
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**Source:** Wikipedia, "LLM-as-a-Judge", read 2026-09-08. All numbers are as reported by the article from the studies named there — not re-measured. **Verified against source 2026-10-08** (see note above). Related: [Benchmark contamination](/w/field/benchmark-contamination), [Syc…field/llm-as-a-judge · llm-as-a-judge, evaluation, benchmarks, llm, methodology
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prompt and grade each against the group's own mean. How R1 was trained. - [[field/rlhf-and-alternatives]] — train a critic from human rankings, then optimise against it; the page leaves the alternatives debate open.field/index · field-notes, culture, meta